With the rapid advancement of AI-generated videos, distinguishing synthetic content from genuine human-driven content has become increasingly difficult, threatening the integrity of human authenticity and creative expression. In this context, Cybernetic Avatar (CA) is introduced as a digital entity that mirrors a remote operator’s facial expressions, gestures, and speech in virtual environments, posing new challenges for secure identity verification. A critical threat emerges when unauthorized users manipulate a CA, potentially deceiving both systems and human observers. This paper addresses the CA Authentication problem, which seeks to verify the true teleoperator behind a CA video despite the CA’s mutable appearance and expressive behaviors. More specifically, we propose a robust CA authentication framework that leverages spatio-temporal facial behavior captured from the CA video to authenticate the legitimate teleoperator. To effectively learn identity-sensitive motion patterns (signature), we develop a Behavior Signature Decoder Graph Convolutional Network (BSDec-GCN) that constructs a constrained spatio-temporal graph to amplify identity-specific dynamics and suppress inter-user ambiguity. Furthermore, we introduce a dual landmark and graph-level losses that boost discrimination. The comprehensive experiments and the thorough ablation studies demonstrate the reliability of the proposed framework with a competitive performance against the existing baseline methods. To the best of our knowledge, this work presents the first graph-based learning approach tailored for Cybernetic Avatar authentication, opening a new direction for securing virtual identity in the era of AI-mediated communication.

Facial diagnosis has been widely adopted over the last decade, yet the corresponding privacy concerns, particularly regarding the leakage of patient facial and biological data, have not been widely addressed. Other soft biometrics like age and gender shared common underlying features with the disease prediction task, offering a potential multitask learning paradigm for disease detection. Another critical challenge lies in the need for lightweight models to be deployed at the edge devices at medical institutions, which frequently requires limited resources and stricter real-time processing demands. Addressing these challenges, we proposed a Privacy-preserving Multitask Network with Shared Attention (PMNet-SA) for facial-based diagnosis. Here, a facial-based privacy-preserving, lightweight multitask learning network for multi-class classification of LMD subtypes (Hyperlipidemia, Fatty Liver Disease, and Dyslipidemia, and with healthy controls) is established, while concurrently estimating age and gender. The proposed lightweight model employed an attention mechanism with multitask learning ability with age and gender, with fast inference time, which is suitable for facial-based diagnosis. Furthermore, we empirically prove that there exists a mutual effect between age, gender, and disease prediction, which helps the facial-based diagnosis capability. The experiments show that our proposed model achieves an overall accuracy of 91.15%, outperforming a number of advanced models.

Over the last decades, finger vein biometric recognition has generated increasing attention because of its high security, accuracy, and natural anti-counterfeiting. However, most of the existing finger vein recognition approaches rely on image enhancement or require much prior knowledge, which limits their generalization ability to different databases and different scenarios. Additionally, these methods rarely take into account the interference of noise elements in feature representation, which is detrimental to the final recognition results. To tackle these problems, we propose a novel jointly embedding model, called Joint Discriminative Analysis with Low-Rank Projection (JDA-LRP), to simultaneously extract noise component and salient information from the raw image pixels. Specifically, JDA-LRP decomposes the input image into noise and clean components via low-rank representation and transforms the clean data into a subspace to adaptively learn salient features. To further extract the most representative features, the proposed JDA-LRP enforces the discriminative class-induced constraint of the training samples as well as the sparse constraint of the embedding matrix to aggregate the embedded data of each class in their respective subspace. In this way, the discriminant ability of the jointly embedding model is greatly improved, such that JDA-LRP can be adapted to multiple scenarios. Comprehensive experiments conducted on three commonly used finger vein databases and four palm-based biometric databases illustrate the superiority of our proposed model in recognition accuracy, computational efficiency, and domain adaptation.

Multiview multi-instance multilabel learning (M3L) is a popular research topic during the past few years in modeling complex real-world objects such as medical images and subtitled video. However, existing M3L methods suffer from relatively low accuracy and training efficiency for large datasets due to several issues: 1) the viewwise intercorrelation (i.e., the correlations of instances and/or bags between different views) are neglected; 2) the diverse correlations (e.g., viewwise intercorrelation, interinstance correlation, and interlabel correlation) are not jointly considered; and 3) high computation burden for training process over bags, instances, and labels across different views. To resolve these issues, a novel framework called fast broad M3L (FBM3L) is proposed with three innovations: 1) utilization of viewwise intercorrelation for better modeling of M3L tasks while existing M3L methods have not considered; 2) based on graph convolutional network (GCN) and broad learning system (BLS), a viewwise subnetwork is newly designed to achieve joint learning among the diverse correlations; and 3) under BLS platform, FBM3L can learn multiple subnetworks jointly across all views with significantly less training time. Experiments show that FBM3L is highly competitive (or even better than) in all evaluation metrics [up to 64% in average precision (AP)] and much faster than most M3L (or MIML) methods (up to 1030 times), especially on large multiview datasets (≥260 K objects).

Graph convolutional networks (GCNs) are a popular approach to learn the feature embedding of graph-structured data, which has shown to be highly effective as well as efficient in performing node classification in an inductive way. However, with massive nongraph-organized data existing in application scenarios nowadays, it is critical to exploit the relationships behind the given groups of data, which makes better use of GCN and broadens the application field. In this article, we propose the f uzzy g raph s ubspace c onvolutional n etwork (FGSCN) to provide a brand-new paradigm for feature embedding and node classification with graph convolution (GC) when given an arbitrary collection of data. The FGSCN performs GC on the f uzzy s ubspace ($\mathcal {F}$-space), which simultaneously learns from the underlying subspace information in the low-dimensional space as well as its neighborliness information in the high-dimensional space. In particular, we construct the fuzzy homogenous graph$\mathcal {G}_{\mathcal {F}}$on the$\mathcal {F}$-space by fusing the homogenous graph of neighborliness$\mathcal {G}_{\mathcal {N}}$and homogenous graph of subspace$\mathcal {G}_{\mathcal {S}}$(defined by the affinity matrix of the low-rank representation). Here, it is proven that the GC on$\mathcal {F}$-space will propagate both the local and global information through fuzzy set theory. We evaluated FGSCN on 15 unique datasets with different tasks (e.g., feature embedding, visual recognition, etc.). The experimental results showed that the proposed FGSCN has significant superiority compared with current state-of-the-art methods.

The sparsity is an attractive property that has been widely and intensively utilized in various image processing fields (e.g., robust image representation, image compression, image analysis, etc.). Its actual success owes to the exhaustive mining of the intrinsic (or homogenous) information from the whole data carrying redundant information. From the perspective of image representation, the sparsity can successfully find an underlying homogenous subspace from a collection of training data to represent a given test sample. The famous sparse representation (SR) and its variants embed the sparsity by representing the test sample using a linear combination of training samples with $L_{0}$ -norm regularization and $L_{1}$ -norm regularization. However, although these state-of-the-art methods achieve powerful and robust performances, the sparsity is not fully exploited on the image representation in the following three aspects: 1) the within-sample sparsity, 2) the between-sample sparsity, and 3) the image structural sparsity. In this paper, to make the above-mentioned multi-context sparsity properties agree and simultaneously learned in one model, we propose the concept of consensus sparsity (Con-sparsity) and correspondingly build a multi-context sparse image representation (MCSIR) framework to realize this. We theoretically prove that the consensus sparsity can be achieved by the $L_{\infty }$ -induced matrix variate based on the Bayesian inference. Extensive experiments and comparisons with the state-of-the-art methods (including deep learning) are performed to demonstrate the promising performance and property of the proposed consensus sparsity.

The Collaborative Representation (CR) framework has provided various effective and efficient solutions to pattern analysis. By leveraging between discriminative coefficient coding (l 2 regularization) and the best reconstruction quality (collaboration), the CR framework can exploit discriminative patterns efficiently in high-dimensional space. Due to the limitations of its linear representation mechanism, the CR must sacrifice its superior efficiency for capturing the non-linear information with the kernel trick. Besides this, even if the coding is indispensable, there is no mechanism designed to keep the CR free from inevitable noise brought by real-world information systems. In addition, the CR only emphasizes exploiting discriminative patterns on coefficients rather than on the reconstruction. To tackle the problems of primitive CR with a unified framework, in this article we propose the Euler Collaborative Representation (E-CR) framework. Inferred from the Euler formula, in the proposed method, we map the samples to a complex space to capture discriminative and non-linear information without the high-dimensional hidden kernel space. Based on the proposed E-CR framework, we form two specific classifiers: the Euler Collaborative Representation based Classifier (E-CRC) and the Euler Probabilistic Collaborative Representation based Classifier (E-PROCRC). Furthermore, we specifically designed a robust algorithm for E-CR (termed as R-E-CR ) to deal with the inevitable noises in real-world systems. Robust iterative algorithms have been specially designed for solving E-CRC and E-PROCRC. We correspondingly present a series of theoretical proofs to ensure the completeness of the theory for the proposed robust algorithms. We evaluated E-CR and R-E-CR with various experiments to show its competitive performance and efficiency.

The human breath has various components to show an individual’s health status or represent a concrete disease, such as kidney disease or diabetes mellitus. Moreover, electronic nose (e-nose) is a widely used method to evaluate the breath sample of people with various chemical sensors to reflect their current health situation or predict various illnesses. Thus, e-nose breath analysis is a convenient, low-cost approach at present. So far, many works focus on various disease detection, such as diabetes, lung cancer, and kidney disease via breath sample analysis. However, few studies aim to investigate multi-feature fatty liver disease detection by evaluating breath samples via the e-nose. In this study, we propose a multi-feature representation method to extract multiple features for diagnosing fatty liver from healthy candidates via breath sample analysis. In particular, two external features, i.e., low-dimensional and latent as well as one internal feature, i.e., channel are extracted from the breath sample, which are further concatenated before being applied to various classifiers for diagnosis. Experimental results indicate that our proposed approach can obtain solid performances (Accuracy of 72.39% with SVM) in detecting fatty liver disease compared to only applying single feature methods.

Binary feature representation methods have received increasing attention due to their high efficiency and great robustness to illumination variation. However, most of them are hand-designed feature descriptors that generally require much prior knowledge in their design. This paper introduces a Row-sparsity Binary Feature Learning (Rs-BFL) method to adaptively learn and encode palmprint features for open-set palmprint recognition. Given the training palmprint images, RsBFL jointly learns a bank of linear projection functions that transform the informative texture features into discriminative binary codes. Afterwards, we calculate the block-wise histograms of each feature map and concatenate them as the final feature representation. Based on the pre-trained projection matrix, we mapped the palmprint texture features of the test samples into binary features for matching. For RsBFL, we enforce three criteria: 1) the quantization error between the projected real-valued features and the binary features is minimized, at the same time, the projection noise is minimized; 2) the latent label semantic information is utilized to minimize the distance of the within-class samples and simultaneously maximize the distance of the between-class samples; 3) the$l_{2,1}$norm is used to make the projection matrix to extract more discriminative features. Extensive experimental results on two publicly accessible palmprint datasets demonstrated the effectiveness and powerful learning capability of the proposed method.

Latent subspace learning aims to produce a latent representation for better reconstruction and classification from high-dimensional data through exploiting the optimal subspace. Current latent subspace learning methods commonly have three problems: 1) The discriminative property is ignored when learning the latent subspace, 2) The redundancy exists between the latent subspace and the prediction space, 3) There is no unified latent subspace that exploits knowledge jointly from the raw space, latent subspace, and label space. In this paper, we formulate theJointDiscriminativeLatentSubspaceLearning (JDLSL) problem to address these issues, and provide its optimization solution. JDLSL learns image representation from two aspects: a) the joint learning of latent subspaces for data reconstruction and prediction, b) the joint learning of label space and latent subspace for data reconstruction. To integrate knowledge from the joint learning, we organize the sparsity-induced latent subspace, where row-sparsity and column sparsity are simultaneously imposed. We provide the theoretical proof for the discriminativity learning ability of the sparsity-induced latent subspace. Extensive experiments and comparisons with the state-of-the-art showed that the proposed method has better performance. JDLSL shows a competitive performance with deep features compared to deep learning architectures, reflecting it potential integrating with deep learning.

It is well known in Traditional Chinese Medicine (TCM) that a person's wrist pulse signal can reflect their health condition. Recently, many computerized wrist pulse AI systems have been proposed to simulate a practitioner's three fingers in order to acquire the wrist pulse signals (three positions/channels) from a candidate's wrist dynamically, before evaluating their health status based on the various feature extraction and detection methods. However, few works have investigated the correlation of the extracted features from the three wrist channels and comprehensively fused the various features together, which can improve the performance of wrist pulse diagnosis. In this paper, we propose a graph based multichannel feature fusion (GBMFF) method to utilize the multichannel features of the wrist pulse signals effectively. In detail, two different sensors, i.e., pressure and photoelectricity are used to capture the three channels of the wrist pulse signals. These are used to generate two different features by applying the stacked sparse autoencoder and wavelet scattering. Each feature of one wrist pulse sample is regarded as a node associated with its corresponding feature vector, and used to construct a graph for one candidate. A novel algorithm is implemented to construct different graphs for different candidates, which are used for wrist pulse diagnosis by developing graph convolutional networks. Experimental results indicate that our proposed AI-based method can obtain superior performances compared to other state-of-the-art approaches.

Diabetes mellitus and lung cancer are two of the most common fatal diseases in the world, causing considerable deaths every year. However, it is not easy to detect diabetes mellitus and lung cancer efficiently--needing professional medical instruments such as a CT and a qualified individual to perform the Fasting Plasma Glucose test. Considering the risks and various inconveniences with conventional diagnosis methods, noninvasive approaches based on computerized analysis are desired. The aim of this paper is to distinguish patients with diabetes mellitus, lung cancer from healthy people simultaneously by analyzing facial images through the stacked sparse autoencoder. Experimental results on a dataset containing 450 healthy samples, 284 diabetes and 175 lung cancer patients produced the F1-score of 93.57%, 97.54%, 81.56% for detecting healthy, diabetes and lung cancer, respectively, validating the effectiveness of our proposed method.

Currently, diabetes mellitus and breast cancer have become more widespread than ever before. Those suffering from these two types of diseases usually need a blood test or biopsy, where both extract fluids or tissues from the human body, which brings pain and a sense of discomfort. With the rise of medical biometrics, it is possible to perform non-invasive detection according to the biometric identifiers from the face of the patients. However, it is still difficult to simultaneously perform disease detection on both diabetes mellitus and breast cancer accurately. To resolve this issue, in this paper, we propose a progressive stack face-based network (PF-Net) to perform multi-class classification on diabetes mellitus, breast cancer, and healthy control using facial information. To perform diagnosis in a progressive way, a latent facial representation is first generated from a stacked sparse autoencoder. Later, the representation is fed into an ensemble layer containing several classifiers. Finally, only the effective classifiers are activated in the classification layer to make the final decision. The experiments showed our proposed method achieved an overall Accuracy of 92.94%, which outperforms a number of classification methods.