Unrestrained palmprint recognition refers to a comprehensive identity authentication technology, that performs personal authentication based on the palmprint images captured in uncontrolled environments, i.e., smartphone cameras, surveillance footage, or near-infrared scenarios. However, unrestrained palmprint recognition faces significant challenges due to the variability in image quality, lighting conditions, and hand poses present in such settings. We observed that many existing methods utilize the subspace structure as a prior, where the block diagonal property of the data has been proved. In this paper, we consider a unified learning model to guarantee the consensus block diagonal property for all views, named high-confident block diagonal analysis for multi-view palmprint recognition (HCBDA_MPR). Particularly, this paper proposed a multi-view block diagonal regularizer to guide that all views learn a consensus block diagonal structure. In such a manner, the main discriminant features from each view can be preserved while the learning of the strict block diagonal structure across all views. Experimental results on a number of real-world unrestrained palmprint databases proved the superiority of the proposed method, where the highest recognition accuracies were obtained in comparison with the other state-of-the-art related methods.

In the realm of emotion recognition concerning continuous temporal sequence data, scholars have delved into various effective integration strategies from multiple perspectives, yielding commendable results. The majority of these studies have comfortably relied on Long Short-Term Memory (LSTM) networks to extract features from both video and audio, of ten overlooking the thorough extraction of underlying features prior to integration. We have entirely eschewed convolutional and recurrent architectures, opting instead to design a simple, stackable Temporal Slicing Encoder (TSE) to distill temporal characteristics. Empirical evidence from two sentiment analysis datasets demonstrates that the TSE module excels in the extraction of emotional features. Building upon this foundation, we have further explored modality interaction, addressing cross modal data activation and synergy optimization between different features, devising the Deep Bimodal Information Transfer Module (DBIT) and the Dynamic Synergy Optimization Network (DSON), which, in conjunction with the TSE module, form our TASE-Net (Temporal Attention Synergy Emotion Network). The DBIT module establishes a cross-attention mechanism guided by mutual information to facilitate text-guided cross-modal data activation, while the DSON module achieves adaptive emotional feature confidence allocation and knowledge transfer between trimodal and unimodal features through an Emotional Weight Adjuster (EWA) and an Asymmetric Bidirectional Distillator (ABD). Extensive experiments on the CMU-MOSI and CMU MOSEI datasets substantiate the efficacy and advancement of our TASE-Net and TSE encoder.

Owing to its remarkable convenience, weak invasiveness, and strong private security, palmprint recognition has become one of the most promising biometric methods and has attracted increasing attention in both academia and industry. Although considerable recognition performance has been achieved by existing palmprint learning methods, they generally require the use of substantial labeled datasets and involve substantial computational overhead for feature learning. In this article, we propose a novel multilayer projection learning (MLPL) method to achieve efficient palmprint feature learning and recognition. First, we transform the palmprint images into their direction-specific representations by computing the difference in the multiple directional responses. Then, we learn three layers of feature projections for robust feature learning, including low-rank projection for image noise decoupling, feature projection for discriminative feature exploration, and quantization projection for information preservation during feature encoding. With multilayer feature projections, palmprint images can be transformed into discriminative feature representations through a single-step process for efficient palmprint recognition. Moreover, we extend the proposed MLPL, referred to as E-MLPL, by minimizing the representation discrepancy between heterogeneous palmprint images to make it applicable for heterogeneous palmprint recognition. The results obtained from five widely adopted databases confirm the superior performance of the proposed method in terms of both accuracy and efficiency.

Palm-vein has received widespread attention for reliable biometric recognition due to its robust resistance to replicate and forge. However, the rise of adversarial attacks poses a high risk of vulnerability for palm-vein recognition, leaving most existing methods vulnerable to small and human-imperceptible adversarial perturbations. In this paper, we propose a palm-vein image reconstruction network for palm-vein image protection, which mainly consists of palm-vein-specific exploration, feature refinement, and image reconstruction sub-networks. Specifically, we first specially learn the noise-insensitive palm-vein-specific feature by decoupling non-vein noise information via cascaded noise-injected and Canny-based convolution layers, and then refine palm-vein-specific features via multiple stacked basic convolution and transposed convolution pairs. Lastly, we convert the fine-grained palm-vein features into the latent sharp palm-vein images via two transposed convolution layers. Moreover, we develop both identity-aware and visual-aware loss functions to ensure the high-quality of the reconstructed palm-vein images. Experimental results on the widely used PolyU palm-vein dataset demonstrate the promising effectiveness of the proposed palm-vein image reconstruction network.

Gait recognition systems are increasingly deployed in real-world scenarios where adversarial robustness is critical but remains underexplored. To this end, we propose DAGait, a novel adversarial defense framework for gait recognition, which operates in the gait embedding space to address the incompatibility of conventional image-space defenses with metric-based gait models. Specifically, a feature-level progressive adversarial training strategy (FPA) is proposed, which applies bounded perturbations and gradually integrates adversarial examples into the training phase, enabling a smooth transition from clean to robust representations. We further propose TriWeight, a novel triplet loss that emphasizes adversarially hard examples while employing three dynamic weights to mitigate view variations and sample interference. Extensive experiments conducted on the CASIA-B* dataset demonstrate that DAGait significantly enhances adversarial robustness, offering a practical and effective solution for secure gait-based biometric systems.

Uncontrolled palmprint recognition faces significant challenges due to the variability in image quality, lighting conditions, and hand poses present in such settings. Traditional multi-view palmprint representation methods enhance the robustness by fusing comprehensive features from multiple perspectives. However, it is still an issue that unifies these diverse views into a coherent and consistent representation. Aiming to solve these problems, in this paper, a unified learning model, named multi-view block diagonal structure learning based uncontrolled palmprint recognition (MBDSL_UPR), was proposed to guarantee the multi-view block diagonal property for the feature matrices from multiple views. For this purpose, we first introduced a multi-view block diagonal structure decomposition strategy to learn strict block diagonal representation for each view. Afterwards, an structure alignment paradigm was designed to preserve a consensus structure for features from all views. Experimental results on a number of real-world unconstrained palmprint databases proved the superiority of the proposed method, where the highest recognition accuracies were obtained in comparison with the other state-of-the-art related methods.

Current existing clustering methods for handling incomplete multi-view data primarily concentrate on learning a common representation or graph from the available views, while overlooking the latent information contained in the missing views and the imbalance of information among different views. Furthermore, instances with weak discriminative features usually degrading the precision of consistent representation or graph across all views. To address these problems, in this paper, we propose a simple but efficient method, called high-confident local structure guided consensus graph learning for incomplete multi-view clustering (HLSCG_IMC). Specifically, this method can adaptively learn a strict block diagonal structure from the available samples using a block diagonal representation regularizer. Different from the existing methods using a simple pairwise affinity graph for structure construction, we consider the influence of instances located at the edge of two clusters on the construction of graph for each view. By harnessing the proposed high-confident strict block diagonal structures, the approach seeks to directly guide the learning of the robust consensus graph. A number of experiments have been conducted to verify the efficacy of our approach.

Three significant challenges have been limiting the stable palmprint recognition via mobile devices: 1) rotations and unconsensus scales of the unconstrait hand; 2) noises generated in the open imaging environments; and 3) low quality images captured in the low-illumination conditions. Current palmprint representation methods rely on rich prior knowledge and lack any adaptability to its environment. In this paper, we propose a multi-view hierarchical graph learning based palmprint recognition (MVHG_PR) method, which comprehensively presents the discriminant palmprint features from multiple views. Fully exploiting different types of characteristics, it aims to adaptively perform multi-view feature description and feature selection. To this end, a novel regularized heterogeneous graph learning strategy is proposed for construction of the intra- and inter-class relationships, learning high-order structures for different views between four tuples, rather than just pair-wise intrinsic structures. In the proposed model, the learned hierarchical graph is given an elastic power from the label information to precisely reflect the intra-class and the inter-class relationships in each view, such that the projected structures can be aligned locally and globally. Besides this, we constructed a mobile palmprint dataset to simulate as many open application circumstance as possible to verify the effectiveness of contactless palmprint recognition methods. Experimental results have proven the superiority of the proposed MVHG_PR by achieving the best recognition performances on a number of real-world palmprint databases. The proposed mobile palmprint database and the code of the proposed MVHG_PR are available athttps://github.com/ShupingZhao/MVHG_PR-for-contactless-palmprint-recognition.

Multi-view clustering (MVC) aims to exploit the latent relationships between heterogeneous samples in an unsupervised manner, which has served as a fundamental task in the unsupervised learning community and has drawn widespread attention. In this work, we propose a new deep multi-view contrastive clustering method via graph structure awareness (DMvCGSA) by conducting both instance-level and cluster-level contrastive learning to exploit the collaborative representations of multi-view samples. Unlike most existing deep multi-view clustering methods, which usually extract only the attribute features for multi-view representation, we first exploit the view-specific features while preserving the latent structural information between multi-view data via a GCN-embedded autoencoder, and further develop a similarity-guided instance-level contrastive learning scheme to make the view-specific features discriminative. Moreover, unlike existing methods that separately explore common information, which may not contribute to the clustering task, we employ cluster-level contrastive learning to explore the clustering-beneficial consistency information directly, resulting in improved and reliable performance for the final multi-view clustering task. Extensive experimental results on twelve benchmark datasets clearly demonstrate the encouraging effectiveness of the proposed method compared with the state-of-the-art models.

In cross-domain recognition tasks, the divergent distributions of data acquired from various domains degrade the effectiveness of knowledge transfer. Additionally, in practice, cross-domain data also contain a massive amount of redundant information, usually disturbing the training processes of cross-domain classifiers. Seeking to address these issues and obtain efficient domain-invariant knowledge, this paper proposes a novel cross-domain classification method, named cross-scatter sparse dictionary pair learning (CSSDL). Firstly, a pair of dictionaries is learned in a common subspace, in which the marginal distribution divergence between the cross-domain data is mitigated, and domain-invariant information can be efficiently extracted. Then, a cross-scatter discriminant term is proposed to decrease the distance between cross-domain data belonging to the same class. As such, this term guarantees that the data derived from same class can be aligned and that the conditional distribution divergence is mitigated. In addition, a flexible label regression method is introduced to match the feature representation and label information in the label space. Thereafter, a discriminative and transferable feature representation can be obtained. Moreover, two sparse constraints are introduced to maintain the sparse characteristics of the feature representation. Extensive experimental results obtained on public datasets demonstrate the effectiveness of the proposed CSSDL approach.

Palmprint-based biometric recognition has gained widespread attention due to its rich features, contactless acquisition, and low invasiveness. However, most existing methods neglect image quality, making them less effective for low-quality, noisy palmprint images. In this paper, we propose a palm intrinsic features learning selective state space model (PalmMamba) for palmprint image denoising, which consists of shallow feature representation, noise-insensitive palmprint-specific feature learning, and sharp palmprint image restoration modules. First, we convert the degraded noisy palmprint image into a high-dimensional shallow feature representation through a single-layer convolution backbone. Then, we develop parallel learning branches, including a second-order attention-based selective state space model and a mixed difference convolution module, to exploit diverse palmprint-specific features with both global and local details. Finally, we map the fine-grained palmprint-intrinsic feature map into the identity-preserved sharp palmprint image via a commonly used convolution layer. Extensive experimental results on five public palmprint databases demonstrate the encouraging performance of the proposed PalmMamba in palmprint image denoising.

Cross-modal hash learning has drawn widespread attention for large-scale multimodal retrieval because of its stability and efficiency in approximate similarity searches. However, most existing cross-modal hashing approaches employ discrete label-guided information to coarsely reflect intra- and intermodality correlations, making them less effective to measuring the semantic similarity of data with multiple modalities. In this paper, we propose a new heterogeneous pairwise-semantic enhancement hashing (HPsEH) for large-scale cross-modal retrieval by distilling higher-level pairwise-semantic similarity from supervision information. First, we adopt a supervised self-expression to learn a data-specific quantified semantic matrix, which uses real values to measure both the similarity and dissimilarity ranks of paired instances, such that the intrinsic semantics of the data can be well captured. Then, we fuse the label-based information and quantified semantic similarity to collaboratively learn the hash codes of multimodal data, such that both the intermodality consistency and modality-specific features can be simultaneously obtained during hash code learning. Moreover, we employ effective iterative optimization to address the discrete binary solution and massive pairwise matrix calculation, making the HPsEH scalable to large-scale datasets. Extensive experimental results on three widely used datasets demonstrate the superiority of our proposed HPsEH method over most state-of-the art approaches.

Graph-based multi-view clustering methods have gained significant attention due to their outstanding ability of clustering-structure representation. Considering the influence on the quality of pre-constructed graphs by noise, in this paper, we propose a novel method called ANGVR. Unlike existing methods that aim to directly learn a consensus graph from multi-view data, ANGVR rebuilds the graph constructed from raw data to seek a consensus graph across views for clustering. Furthermore, to guide the construction of graph, an embedding constraint based on neighboring group structures is introduced, which explores the neighborhood structure information corresponding to neighborhood sets. The experimental results show improvements in accuracy of 5.56%, 6.77% and 4.02%, respectively on 3Sources dataset with 10%, 30% and 50% missing view compared to the existing works.

Abstract Many subspace learning methods based on low-rank representation employ the nearest neighborhood graph to preserve the local structure. However, in these methods, the nearest neighborhood graph is a binary matrix, which fails to precisely capture the similarity between distinct samples. Additionally, these methods need to manually select an appropriate number of neighbors, and they cannot adaptively update the similarity graph during projection learning. To tackle these issues, we introduce Discriminative Subspace Learning with Adaptive Graph Regularization (DSL_AGR), an innovative unsupervised subspace learning method that integrates low-rank representation, adaptive graph learning and nonnegative representation into a framework. DSL_AGR introduces a low-rank constraint to capture the global structure of the data and extract more discriminative information. Furthermore, a novel graph regularization term in DSL_AGR is guided by nonnegative representations to enhance the capability of capturing the local structure. Since closed-form solutions for the proposed method are not easily obtained, we devise an iterative optimization algorithm for its resolution. We also analyze the computational complexity and convergence of DSL_AGR. Extensive experiments on real-world datasets demonstrate that the proposed method achieves competitive performance compared with other state-of-the-art methods.

More and more product designers are adopting anthropomorphic design strategies to facilitate the widespread use of AI assistants. However, existing studies show that the ideal boundary of the degree of anthropomorphism is still unclear. Therefore, we design two scenario experimental studies to explore the influence of different degrees of anthropomorphism on human–AI interaction quality. We also consider the difference in impact between two usage contexts: the hedonic and utilitarian contexts. The results show that different degrees of anthropomorphism significantly affect human–AI interaction quality in different ways; when AI assistants have a medium-level degree of anthropomorphism, the positive effect is pronounced. Furthermore, the positive relationship between anthropomorphism and human–AI interaction quality is more robust in the hedonic usage context; this positive effect disappears in the utilitarian usage context. We expect this study to provide practical guidance for AI product designers to achieve their products’ long-term feasibility and sustainability.

Falls are a major health threat for older people. A timely assistance can reduce the extent of physical injury caused by the falls. Currently, low-cost and convenient video surveillance systems based on ordinary RGB cameras are widely used for improving the safety of people. The fall detection is a research hotspot in intelligent video surveillance. In this work, we propose an unsupervised fall detection method. The proposed method first converts the RGB video frames into human pose images to eliminate the background interferences and focus on human motion and protect privacy. Afterwards, the future pose images are predicted by using the continuous historical human pose images based on a constrained generative adversarial network (GAN). Finally, the prediction errors of the human pose images and the anomaly scores of actual poses calculated by using the traditional hand-crafted features are used to realize the fall detection. As compared to the existing vision-based fall detection methods, the proposed method possesses strong generalization ability, and is robust to environmental interferences and small local occlusions, and effectively protects the privacy, and avoids time-consuming data annotations. In addition, in this work, a new large-scale and comprehensive fall dataset is created and is available for download. We perform extensive experiments on the public benchmark datasets and the proposed dataset. The results demonstrate the validity and superiority of the proposed method.

Palmprint recognition has shown great value for biometric recognition due to its advantages of good hygiene, semi-privacy and low invasiveness. However, most existing palmprint recognition studies focus only on homogeneous palmprint recognition, where comparing palmprint images are collected under similar conditions with small domain gaps. To address the problem of matching heterogeneous palmprint images captured under the visible light (VIS) and the near-infrared (NIR) spectrum with large domain gaps, in this paper, we propose a Fourier-based feature learning network (FFLNet) for VIS-NIR heterogeneous palmprint recognition. First, we extract the multi-scale shallow representations of heterogeneous palmprint images via three vanilla convolution layers. Then, we convert the shallow palmprint feature maps into frequency-specific representations via Fourier transform to separate different layers of palmprint features, and exploit the underlying common and palmprint-specific frequency information of heterogeneous palmprint images. This effectively reduces the modality gap of heterogeneous palmprint images at the feature level. After that, we convert the common frequency-specific feature maps back to the spatial domain to learn the identity-invariant discriminative features via residual convolution for heterogeneous palmprint recognition. Extensive experimental results on three challenging heterogeneous palmprint databases clearly demonstrate the effectiveness of the proposed FFLNet for VIS-NIR heterogeneous palmprint recognition.

Unconstrained palmprint images have shown great potential for recognition applications due to their lower restrictions regarding hand poses and backgrounds during contactless image acquisition. However, they face two challenges: 1) unclear palm contours and finger-valley points of unconstrained palmprint images make it difficult to locate landmarks to crop the palmprint region of interest (ROI); and 2) large intra-class diversities of unconstrained palmprint images hinder the learning of intra-class-invariant palmprint features. In this paper, we propose to directly extract the complete palmprint region as the ROI (CROI) using the detection-style CenterNet without requiring the detection of any landmarks, and large intra-class diversities may occur. To address this, we further propose a palmprint feature alignment and learning hybrid network (PalmALNet) for unconstrained palmprint recognition. Specifically, we first exploit and align the multi-scale shallow representation of unconstrained palmprint images via deformable convolution and alignment-aware supervision, such that the pixel gaps of the intra-class palmprint CROIs can be minimized in shallow feature space. Then, we develop multiple triple-attention learning modules by integrating spatial, channel, and self-attention operations into convolution to adaptively learn and highlight the latent identity-invariant palmprint information, enhancing the overall discriminative power of the palmprint features. Extensive experimental results on four challenging palmprint databases demonstrate the promising effectiveness of both the proposed PalmALNet and CROI for unconstrained palmprint recognition.

Since hand-print recognition, i.e., palmprint, finger-knuckle-print (FKP), and hand-vein, have significant superiority in user convenience and hygiene, it has attracted greater enthusiasm from researchers. Seeking to handle the long-standing interference factors, i.e., noise, rotation, shadow, in hand-print images, multi-view hand-print representation has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. However, the existing methods usually ignore the high-order correlations between different views or fuse very limited types of features. To tackle these issues, in this paper, we present a novel tensorized multi-view low-rank approximation based robust hand-print recognition method (TMLA_RHR), which can dexterously manipulate the multi-view hand-print features to produce a high-compact feature representation. To achieve this goal, we formulate TMLA_RHR by two key components, i.e., aligned structure regression loss and tensorized low-rank approximation, in a joint learning model. Specifically, we treat the low-rank representation matrices of different views as a tensor, which is regularized with a low-rank constraint. It models the across information between different views and reduces the redundancy of the learned sub-space representations. Experimental results on eight real-world hand-print databases prove the superiority of the proposed method in comparison with other state-of-the-art related works.

Low-quality palmprint images will degrade the recognition performance, when they are captured under the open, unconstraint, and low-illumination conditions. Moreover, the traditional single-view palmprint representation methods have been difficult to express the characteristics of each palm strongly, where the palmprint characteristics become weak. To tackle these issues, in this article, we propose a structure suture learning-based robust multiview palmprint recognition method (SSL_RMPR), which comprehensively presents the salient palmprint features from multiple views. Unlike the existing multiview palmprint representation methods, SSL_RMPR introduces a structure suture learning strategy to produce an elastic nearest neighbor graph (ENNG) on the reconstruction errors that simultaneously exploit the label information and the latent consensus structure of the multiview data, such that the discriminant palmprint representation can be adaptively enhanced. Meanwhile, a low-rank reconstruction term integrating with the projection matrix learning is proposed, in such a manner that the robustness of the projection matrix can be improved. Particularly, since no extra structure capture term is imposed into the proposed model, the complexity of the model can be greatly reduced. Experimental results have proven the superiority of the proposed SSL_RMPR by achieving the best recognition performances on a number of real-world palmprint databases.

Palmprint has attracted increasing attention for biometric recognition in recent years due to its outstanding reliability, user-friendliness and hygiene. However, existing palmprint recognition methods usually require high-quality palmprint images with clear texture and line patterns; however, in practical applications palmprint images are usually of low quality. In this study, we propose a dense hybrid attention (DHA) network for palmprint image super-resolution (SR) by recovering the clear palmprint-specific characteristics. The proposed DHA network first obtains the high-dimensional shallow representation via a single convolution layer, and then jointly learns the local and global palmprint-specific features via parallel convolutional neural network (CNN)-and transformer-based branches. Particularly, we develop two enhanced spatial and channel attention (CA) modules to adaptively emphasize the local position-specific characteristics of palmprints, such that the SR palmprint images can be well recovered with clear texture and edge characteristics. Experimental results on three publicly used palmprint databases clearly show the effectiveness of the proposed method for palmprint image SR.

Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore the important information hidden in the missing views and the latent intrinsic structures in each view. To tackle these issues, in this paper, a unified and novel framework, named tensorized incomplete multi-view clustering with intrinsic graph completion (TIMVC_IGC) is proposed. Firstly, owing to the effectiveness of the low-rank representation in revealing the inherent structure of the data, we exploit it to infer the missing instances and construct the complete graph for each view. Afterwards, inspired by the structural consistency, a between-view consistency constraint is imposed to guarantee the similarity of the graphs from different views. More importantly, the TIMVC_IGC simultaneously learns the low-rank structures of the different views and explores the correlations of the different graphs in a latent manifold sub-space using a low-rank tensor constraint, such that the intrinsic graphs of the different views can be obtained. Finally, a consensus representation for each sample is gained with a co-regularization term for final clustering. Experimental results on several real-world databases illustrates that the proposed method can outperform the other state-of-the-art related methods for incomplete multi-view clustering.

In this article, we propose a novel and effective incomplete multi-view clustering (IMVC) framework, referred to as incomplete multi-view clustering with regularized hierarchical graph (IMVC_RHG). Different from the existing graph learning-based IMVC methods, IMVC_RHG introduces a novel heterogeneous-graph learning and embedding strategy, which adopts the high-order structures between four tuples for each view, rather than a simple paired-sample intrinsic structure. Besides this, with the aid of the learned heterogeneous graphs, a between-view preserving strategy is designed to recover the incomplete graph for each view. Finally, a consensus representation for each sample is gained with a co-regularization term for final clustering. As a result of integrating these three learning strategies, IMVC_RHG can be flexibly applied to different types of IMVC tasks. Comparing with the other state-of-the-art methods, the proposed IMVC_RHG can achieve the best performances on real-world incomplete multi-view databases.

In recent years, smart home assistants have been used by a large number of people due to their simple, hands-free, voice-based operation. To ensure the long-term success and widespread dissemination of a product, it is important to evaluate its continued use. This study is mainly based on uses and gratifications theory to explore the relationship between the initial use of, gratification provided by, and continued use intention of smart home assistants, and to analyze differences in use by different age groups. The results confirm that different types of SHAs use lead to different levels of gratification in different categories. And gratification of different categories of users has a significant positive impact on the continued use intention. In addition, significant differences exist in the impact path of using smart home assistants to alleviate loneliness, among different age groups.

Compared with uni-modal biometrics systems, multimodal biometrics systems using multiple sources of information for establishing an individual’s identity have received considerable attention recently. However, most traditional multimodal biometrics techniques generally extract features from each modality independently, ignoring the implicit associations between different modalities. In addition, most existing work uses hand-crafted descriptors that are difficult to capture the latent semantic structure. This paper proposes to learn the sparse and discriminative multimodal feature codes (SDMFCs) for multimodal finger recognition, which simultaneously takes into account the specific and common information among inter-modality and intra-modality. Specifically, given the multimodal finger images, we first establish the local difference matrix to capture informative texture features in local patches. Then, we aim to jointly learn discriminative and compact binary codes by constraining the observations from multiple modalities. Finally, we develop a novel SDMFC-based multimodal finger recognition framework, which integrates the local histograms of each division block in the learned binary codes together for classification. Experimental results on three commonly used finger databases demonstrate the effectiveness and robustness of the proposed framework in multimodal biometrics tasks.

In the real-world, some views of samples are often missing for the collected multiview data. Faced with the incomplete multiview data, most of the existing clustering methods tended to learn a common graph from the available views, where the hidden information of the absent views was ignored. Furthermore, some methods filled the absent instances with the average vector of the available samples for each view, which could not reflect a real distribution of the data. To solve these problems, in this paper an intrinsic and complete structure learning based incomplete multiview clustering method (ICSL_IMC) is proposed. Firstly, we calculate the initial complete graphs for all views by exploring the available incomplete graphs, which are further taken as the constraints for the reconstruction of the absent data integrating the self-representation method. Afterwards, encouraged by the complete multiview data, a complete structure inferring strategy is proposed to learn the intrinsic and complete structures for all views, such that the real distribution of the absent instances can be reflected in the completed structure of each view. We integrate these three learning phases into a joint optimization model, which can promote each other in the iterative learning procedure, simultaneously. Comparing with the other state-of-the-art methods, the proposed ICSL_IMC can achieve the best performances on different databases.

In this article, we propose a collaborative palmprint-specific binary feature learning method and a compact network consisting of a single convolution layer for efficient palmprint feature extraction. Unlike most existing palmprint feature learning methods, such as deep-learning, which usually ignore the inherent characteristics of palmprints and learn features from raw pixels of a massive number of labeled samples, palmprint-specific information, such as the direction and edge of patterns, is characterized by forming two kinds of ordinal measure vectors (OMVs). Then, collaborative binary feature codes are jointly learned by projecting double OMVs into complementary feature spaces in an unsupervised manner. Furthermore, the elements of feature projection functions are integrated into OMV extraction filters to obtain a collection of cascaded convolution templates that form a single-layer convolution network (SLCN) to efficiently obtain the binary feature codes of a new palmprint image within a single-stage convolution operation. Particularly, our proposed method can easily be extended to a general version that can efficiently perform feature extraction with more than two types of OMVs. Experimental results on five benchmark databases show that our proposed method achieves very promising feature extraction efficiency for palmprint recognition.

Palmprint recognition provides a potential solution for noninvasive personal authentication due to its excellent contactless property and user-security, and it has attracted tremendous research interest in recent years. However, most existing methods focus on intraspectral palmprint recognition, which requires gallery and probe images to be captured under similar illumination, and thus significantly limit its practical applications in open environments with variant illuminations. In this study, we present a spectrum-invariant feature learning method for cross-spectral palmprint recognition to address the problem that gallery and probe samples are captured under different spectra. First, the blockwise direction-based ordinal measure vectors are formed to represent the intrinsic information of palmprint images. Then, a unified feature projection is jointly learned to map two different spectra of palmprint images into a common feature space, in which the different spectral features have enhanced discriminative power by enlarging their variances while the intraclass features learned from different spectral images are similar. The proposed method can be easily extended to seek the unified spectrum-invariant representation of multiple spectral palmprint images, making it feasible to perform palmprint recognition crossing one spectrum to multiple spectra. Experimental results on two multispectral palmprint image databases demonstrate the promising effectiveness of the proposed method on cross-spectral palmprint recognition.

Preserving projection learning has been widely used in feature extraction and selection for unsupervised image classification. Generally, some related methods constructed a graph to represent the nearest neighbor relationships of the data based on the Euclidean distances among different samples, which used 0 or 1 to predefine whether two samples are from the same class. Since a simple Euclidean distance is sensitive to noise, the predefined graph cannot produce exact correlations between the two samples. What is more, the predefined graph cannot reflect the structure of the projected data on a latent subspace when the projection matrix is learned. To solve these problems, in this article a novel adaptive graph embedded preserving projection learning (AGE_PPL) method is proposed, first combining the sparsity-based graph learning and the projection learning as an integral framework for feature extraction and feature selection. In particular, a sparse representation term with$l_{1}$-norm is exploited in AGE_PPL to achieve the adaptive graph of the data to preserve the local structures among different samples while the projection matrix is learned. Meanwhile, a global-scale constraint is imposed to preserve the global structure of the data on a latent subspace. Therefore, the transformed samples will be more discriminative, allowing margins of the same class to be reduced, and margins among different classes to be enlarged. Experimental results proved the effectiveness of the proposed algorithm by obtaining competitive performances over other baseline and state-of-the-art methods. In addition, the proposed method is very flexible for feature selection and dimensionality reduction.

Low-rank based methods have been widely adopted to structure preserving, when the projection matrix is learned for feature extraction. However, some dilemmas still exist that degrade the classification performance: 1) The local structure of the data is ignored; 2) the reconstructed data is not consistent with the original data. To solve those problems, in this paper a weighted graph embedded low-rank projection (WGE_LRP) method is proposed. In WGE_LRP, a novel weighted graph regularization term is proposed, which can learn the local structure of the data based on the similarity of different samples. Meanwhile, an extra global information term is introduced to keep the reconstructed data consistent with the original data. Experimental results show that the proposed method can obtain competitive performance in comparison to the state-of-the-arts.

To address the sparsity problem, a novel collaborative filtering approach based on multiple attribute decision making (MADM-CF) is proposed. In MADM-CF, users in collaborative filtering are treated as decision alternatives, items are treated as attributes. The weight of each item is determined, and the preference similarities between the active user and other users are computed. The preference similarity means that how the users’ preferences are similar on positive ratings and negative ratings. According to the preference similarities, the candidate neighbourhood of the active user is determined. A method to compute overall assessment value is designed, the overall assessment value of each user in the candidate neighbourhood is computed, and users with the smallest overall assessment values are selected as the active user’s nearest neighbours. Finally, the most frequent item recommendation method (MFIR) is used to provide top-N recommendations to the active user. Experimental results based on MovieLens and Netflix datasets show that the proposed approach is superior to existing alternatives.

The finger vein trait has attracted widespread attention for personal authentication in recent years. However, most finger vein verification methods are performed on the single perspective, captured by a monocular near-infrared camera fixed at one side of the finger. Consequently, the contents of a single perspective have few details of the spatial network structure of the finger vein and show noticeable differences even if the posture of the same finger is slightly different. Both of them impact the verification performance. Hence, finger vein images captured from different viewpoints are considered in this work. We first design a low-cost multi-perspective based dorsal finger vein imaging device for data collection. A deep neural network named Hierarchical Content-Aware Network (HCAN) is then proposed to extract the discriminative hierarchical features of the finger vein. Specifically, HCAN is compound of a Global Stem Network (GSN) and a Local Perception Module (LPM). GSN aims to extract the latent global 3D feature from all perspectives through a recurrent neural network. It enables the model to retain the details in previous hidden states by incorporating a memory weighting strategy. LPM is designed to perceive each perspective from the aspect of image entropy. Guided by the entropy loss, LPM captures the prominent local feature and improves the discriminability and robustness of the hierarchical feature. The experimental results on the newly collected THU-MFV database demonstrate the superiority of the proposed method in comparison with other multi-perspective and single-perspective based methods.

Various palmprint recognition methods have been proposed and applied in security, particularly authentication. However, improving the performance of palmprint recognition with insufficient training samples per person is still a challenging task. The undersampling problem limits the application and popularization of palmprint recognition. In this article, by regularly sampling different local regions of the palmprint image, we learn complete and discriminative convolution features by using deep convolutional neural networks (DCNNs). With this powerful palmprint description, a joint constrained least-square regression (JCLSR) framework, which performs representation for each local region of the same palmprint image requiring all regular local regions of the palmprint image to have similar projected target matrices, is presented to exploit the commonality of different patches. The proposed method can well solve the undersampling classification problem in palmprint recognition. Experiments were conducted on the IITD, CASIA, noisy IITD, and PolyU multispectral palmprint databases. It can be seen from the experimental results that the proposed JCLSR consistently outperformed the classical palmprint recognition methods and some subspace learning-based methods for palmprint recognition.

Cross-modal hashing has attracted much attention for cross-modal retrieval and achieved promising performance due to its powerful capacity. Some existing cross-modal hashing methods construct pairwise similarities to represent the relationship of heterogeneous data, which require much computation time and storage space, making them unscalable for large-scale retrieval tasks. In this paper, we propose a novel supervised Discrete Semantic Embedding Hashing (DSEH) for cross-modal retrieval. Specifically, we first learn the common representation of heterogeneous data by embedding the semantic labels into a collective matrix factorization, such that both intra- and inter-modality similarities can be well captured. Then, we learn the hash codes in the discrete space based on the learned common representation via an orthogonal rotation technique. Moreover, we learn the multi-modal hash functions that can efficiently convert out-of-sample instances into unified hash codes. Extensive experimental results on three widely used benchmark databases demonstrate the superiority of the proposed DSEH compared with previous state-of-the-arts.

Palmprint direction patterns have been widely and successfully used in palmprint recognition methods. Most existing direction-based methods utilize the pre-defined filters to achieve the genuine line responses in the palmprint image, which requires rich prior knowledge and usually ignores the vital direction information. In addition, some line responses influenced by noise will degrade the recognition accuracy. Furthermore, how to extract the discriminative features to make the palmprint more separable is also a dilemma for improving the recognition performance. To solve these problems, we propose to learn complete and discriminative direction patterns in this study. We first extract the complete and salient local direction patterns, which contains a complete local direction feature (CLDF) and a salient convolution difference feature (SCDF) extracted from the palmprint image. Afterwards, two learning models are proposed to learn sparse and discriminative directions from CLDF and to achieve the underlying structure for the SCDFs in the training samples, respectively. Lastly, the projected CLDF and the projected SCDF are concatenated forming the complete and discriminative direction feature for palmprint recognition. Experimental results on seven palmprint databases, as well as three noisy datasets clearly demonstrates the effectiveness of the proposed method.

Least squares regression (LSR) has two main issues that greatly limits the improvement of performance: 1) The target matrix is too rigid leading to a large regression error; 2) the underlying geometric structure of the training data is often ignored to learn a more discriminative projection matrix. To solve these dilemmas, this paper presents a discriminant and sparsity based least squares regression with l1regularization (DS_LSR). In DS_LSR, the sparse coefficient matrix of the training data with l1regularization is jointly learned with the projection matrix to make the projection matrix discriminative. In addition, an orthogonal relaxed term is introduced to hold the structure of regression targets while relaxing the rigid label matrix. Extensive experimental results demonstrate the effectiveness of the proposed method in classification accuracy.

3D palmprint-based biometric recognition has drawn growing research attention due to its several merits over 2D counterpart such as robust structural measurement of a palm surface and high anti-counterfeiting capability. However, most existing 3D palmprint descriptors are hand-crafted that usually extract stationary features from 3D palmprint images. In this paper, we propose a feature learning method to jointly learn compact curvature feature descriptor for 3D palmprint recognition. We first form multiple curvature data vectors to completely sample the intrinsic curvature information of 3D palmprint images. Then, we jointly learn a feature projection function that project curvature data vectors into binary feature codes, which have the maximum inter-class variances and minimum intra-class distance so that they are discriminative. Moreover, we learn the collaborative binary representation of the multiple curvature feature codes by minimizing the information loss between the final representation and the multiple curvature features, so that the proposed method is more compact in feature representation and efficient in matching. Experimental results on the baseline 3D palmprint database demonstrate the superiority of the proposed method in terms of recognition performance in comparison with state-of-the-art 3D palmprint descriptors.

Palmprint recognition has been widely applied in security and, particularly, authentication. In the past decade, various palmprint recognition methods have been proposed and achieved promising recognition performance. However, most of these methods require rich a priori knowledge and cannot adapt well to different palmprint recognition scenarios, including contact-based, contactless, and multispectral palmprint recognition. This problem limits the application and popularization of palmprint recognition. In this article, motivated by the least square regression, we propose a salient and discriminative descriptor learning method (SDDLM) for general scenario palmprint recognition. Different from the conventional palmprint feature extraction methods, the SDDLM jointly learns noise and salient information from the pixels of palmprint images, simultaneously. The learned noise enforces the projection matrix to learn salient and discriminative features from each palmprint sample. Thus, the SDDLM can be adaptive to multiscenarios. Experiments were conducted on the IITD, CASIA, GPDS, PolyU near infrared (NIR), noisy IITD, and noisy GPDS palmprint databases, and palm vein and dorsal hand vein databases. It can be seen from the experimental results that the proposed SDDLM consistently outperformed the classical palmprint recognition methods and state-of-the-art methods for palmprint recognition.

This letter presents a method of correcting misaligned seismograms scanned during the digitization process of analog seismograms. The proposed method uses the low-rank matrix recovery technique to seek a Euclidean transformation that can be used to implement the correction. As the rank of a matrix is a natural measure of regularity and symmetry of images, a misaligned seismogram is assumed to be corrected when the rank of the texture extracted from the seismogram itself reaches the minimum. Therefore, the misalignment correction problem can be considered as a matrix rank minimization problem. The augmented Lagrange multiplier is applied to solve this minimization problem because of its fast convergence. Compared with the traditional geometrical methods, our method works efficiently and conveniently as well as overcomes corruptions, such as notes, spots, or marks.

Super-resolution reconstruction (SRR) proposes a fusion of several low-quality images into one higher quality result with better optical resolution. However, due to the vast amount of calculation of the SRR algorithm, its implementation is too slow. In this paper, we present a GPU-based parallel implementation on SRR algorithm. The compute unified device architecture (CUDA) is a programming approach for performing scientific calculations on a graphics processing unit (GPU) as a data-parallel computing device. The proposed GPU-based implementation using CUDA is up to approximately 200 times faster than the corresponding optimized CPU counterparts.