Palmprint recognition systems experience a significant performance decline in cross-domain scenarios due to domain shift caused by non-identity factors such as capture devices and lighting conditions. To address this issue, this paper introduces a novel deep decoupling framework, the Identity and Style Feature Decoupling Network (ISFDNet), designed to improve the model’s cross-domain generalization. ISFDNet explicitly separates stable identity-related information from variable domain-related style information within palmprint features. The framework incorporates two innovative mechanisms: at the feature level, the Spatially-Aware Separation Module (SASM) adaptively produces complementary spatial attention masks to decouple mixed features into identity and style components; at the image level, the Low-Frequency Disturbance Module (LFDM) creates stylized training samples by perturbing the low-frequency parts of images, encouraging the network to learn identity representations that are insensitive to style variations. Additionally, a carefully designed collaborative supervision strategy combines multiple losses to ensure effective decoupling. Extensive experiments on four publicly available palmprint datasets demonstrate that ISFDNet achieves top performance in both cross-domain and in-domain tests, while significantly enhancing the generalization capabilities of existing networks. The code is released at https://github.com/20201422/ISFDNet.

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers’ prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition—often grounded in traditional methodologies—there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This article bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. This article systematically examines progress across key tasks, including region-of-interest (ROI) segmentation, feature extraction, and security and privacy-oriented challenges. Beyond highlighting these advancements, this article identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition.

Palmprint recognition is a pivotal biometric modality, renowned for its numerous advantages and applications in the field of biometrics. The Gabor filter is a classic and efficient texture feature extractor abstracted from the nervous system. The existing palmprint texture coding methods only focus on first-order texture features (1TFs), while neglecting discriminative second-order texture features (2TFs). Therefore, this paper proposes multi-order extensions for state-of-the-art (SOTA) palmprint texture coding methods, which makes full usage of 1TFs and 2TFs. A filter is used to extract 1TFs from the palmprint image, and the same filter is applied to extract 2TFs from 1TFs. Here, different methods employ various filters to extract diverse textures. Due to the simultaneous participations of 1TFs and 2TFs in multi-order extension codes, more discriminative features are extracted and fused. The experimental results on three public databases, including contact, noncontact and multispectral acquisition types, show that the accuracies of all the palmprint texture coding methods are remarkably improved by multi-order extension, establishing it as a general framework extendable to other texture-based recognition tasks.

Palmprint recognition has received considerable attention due to its inherent discriminative characteristics. However, conventional methods largely rely on static features extracted from individual images, which limits their representational richness. To address this, we propose a dynamic palmprint verification framework that harnesses generative models to enhance feature representations through dynamic construction and matching strategies. During training, a classifier-guided generative model synthesizes class-aware pairs, and a regularization term is introduced to expand the feature space, while mitigating overfitting. For matching, we reformulate the process as a subspace projection within a locally adaptive feature space, where the original and class-conditioned generated features form the basis of the subspace. This enables the model to capture latent inter-individual relationships and achieve stronger discriminability. Extensive experiments across multiple backbones and public benchmarks validate the effectiveness and robustness of the proposed framework.

Currently global features are usually extracted directly from local patterns in palmprint verification. Furthermore, sequence features for palmprint verification are only used as local features, but the properties of sequence features are not fully utilized. To solve this issue, this paper introduces Sequence Feature Fusion Network (SF2Net) for palmprint verification. SF2Net proposes a new paradigm: using stable and spatially correlated sequence features as an intermediate bridge to generate robust global representations. SF2Net’s core mechanism is to first extract fine-grained local features that are then converted into sequence features by a sequence feature extractor (SFE). Finally, the sequence features are used as a superior input to capture high-quality global features. By fusing multi-order texture-based local features with globally extracted sequence features, SF2Net achieves superior discrimination. To ensure high accuracy even with limited training data, a hybrid loss function is proposed, which integrate a cross-entropy loss and a triplet loss. Triplet loss effectively optimizes feature separation by explicitly considering negative samples. Extensive experiments on multiple publicly available palmprint datasets demonstrate that SF2Net achieves state-of-the-art (SOTA) performance. Remarkably, even with a small training-to-testing ratio (1:9), SF2Net achieves 100% accuracy, surpassing SOTA methods under several benchmark datasets. The code is released at https://github.com/20201422/SF2Net.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

As a relatively new biometric modality, palmprint attracts much attention for its rich intrinsic features and high commercial prospects. Most existing palmprint recognition methods only extract features from the region of interest (ROI) and neglect the features of other regions. Hence, the recognition performance of these methods highly relies on ROI localization, and it requires users' high cooperation. To relieve these problems, we propose a novel end-to-end recognition framework for contactless whole-palm region-based recognition in this paper, dubbed as Channel Groupwise Drop Network (CGDNet). CGDNet consists of a trunk branch and a part exciting branch. The trunk branch extracts the global representations, and the global scale (GS) module is designed to measure the similarity from feature positions to obtain global knowledge. In the part exciting branch, the features are split into two fine-grained parts equally along the horizontal direction. Then, the channel group-wise drop (CGD) module is designed to aggregate the same part features of the object and cooperates with the CGD mask, which randomly drops the same region in the channel group to excite diverse fine-grained features for learning. Finally, fine-grained features and the global features are concatenated as the final feature. The proposed CGDNet achieves competitive performance in two bench-mark datasets compared with the state-of-the-art methods.

Siamese-based ResNet-driven trackers have made great success in recent years. However, the less discriminative features inhibit the improvements of these trackers. In this work, we discover that a possible reason is the emergence of the gridding artifacts in the deep layers of ResNet-driven trackers. We naively remove the deep layers with gridding artifacts, which reduces parameters by nearly 69% and improves the tracking performance of small objects. Then, we design a parameter-free feature superposition module by adding different samples into the first half samples in each mini-batch iteration in embedding space to increase the semantic information. Further, we introduce an auxiliary loss to reduce the learning difficulty. Finally, the generalization of the feature superposition module and the auxiliary loss are verified by ablation studies. Experiments on challenging benchmarks, including OTB2015, VOT2019, UAV123, LaSOT, and TrackingNet, demonstrate that the proposed method outperforms many SOTA trackers and achieves leading performance.

Siamese-based trackers currently are the dominant tracking paradigm due to the balance between speed and performance. However, it is prone to drift and tracking failure when the environment is complex and similar objects interfere. While the Siamese-based trackers perform the correlation operation, the responses of the target object and background appear in different channels, i.e., the feature spaces of the target object and background have some orthogonality. However, when meeting background clutters and similar objects interfere, this orthogonality becomes weaker and the wrong classification contribution of the object and the background reduces the stability of the learned similarity function, leading to many misclassified pixels in the heatmaps. In this work, we proposed a SiamORPN to solve the above issues. It is incorporated at two levels: an Orthogonal Region Proposal Network (ORPN) and an Adaptive Pixel-wise Aggregation (APA) module. Specifically, for ORPN, the orthogonality between the object and the background maximizes the inter-class inertia. Moreover, the ORPN introduces the orthogonal module to enhance this orthogonality. For APA, it introduces two lightweight networks to predict the weights of all pixels in different heatmaps and the weights of all pixels in different regression offsets. Experiments on challenging benchmarks, including OTB2015, VOT2016, VOT2018, GOT-10k test set, UAV123, LaSOT, and TrackingNet, demonstrate the proposed SiamORPN outperforms many SOTA trackers and achieves leading performance. The inference speed at GTX1080Ti can reach about 32 FPS, meeting the real-time requirements.

Security enhancement via trustworthy identity authentication in Internet of Things (IoT) has soared recently. Biometrics offers a promising remedy to improve the security and utility of IoT and play a role in securing a variety of low-power and limited computing capability IoT devices to address identity management challenges. This article proposes an IoT-compliant co-learned biometric hashing network derived from palm print and palm vein dubbed PalmCohashNet. The PalmCohashNet comprises two hashing subnetworks, one for each palm modality, and is trained collaboratively to generate shared hash codes for respective modality (co-hash codes). A cross-modality hashing (CMH) loss is devised to encourage co-hash codes of palm vein and palm print from the same identity to be adjacent and consistent; meanwhile, pull the co-hash codes of each identity to a preassigned identity-specific hash centroid that is shared by both palm modalities. Two palm-based co-hash codes of a person can be generated simultaneously for deployment. The binary co-hash code is IoT compliant attributed to its highly compact form for storage and fast matching. A trained PalmCohashNet can be flexibly deployed under four operation modes: single-modality matching (print versus print or vein versus vein), multimodality matching where both print and vein are utilized, and cross-modality matching (print versus vein) depending on the IoT service context. Our empirical results on four publicly available palm databases show that the proposed method consistently outperforms state-of-the-art methods.

ABSTRACT Biometric template security and privacy issues are critical in biometric authentication systems and require special attention. However, remote biometric authentication systems demand wider array of measures for maximum protection. This paper proposes a remote cancelable palmprint authentication protocol based on multi‐directional two‐dimensional PalmPhasor‐fusion. The main contribution is three‐fold. First, with a transposition direction selection mechanism, multi‐directional two‐dimensional PalmPhasor (MTDPP) improves the accuracy performance of two‐dimensional PalmPhasor. Second, we provide the theoretical analysis of the effect of transposition on the accuracy performance of two‐dimensional PalmPhasor, and hence establish an effective transposition direction range for the proposed MTDPP. Third, according to our analysis, the existing remote palmprint authentication system does not satisfy non‐invertibility criterion of secure template protection and is vulnerable to interception. Besides, secret message embedding as a countermeasure for database attacks deteriorates accuracy performance and causes inconvenience in updating authenticator. The proposed protocol uses multi‐directional two‐dimensional PalmPhasor‐fusion, one‐time random number encrypted with asymmetric cryptography and encrypted hash codes of MTDPP to address the problems. Copyright © 2013 John Wiley & Sons, Ltd.