Palmprint recognition is one of the most popular biometric technologies. Recent researches mainly focus on the recognition performance, while pay less attention to the data protection issues. In this paper, we propose an active biometric data protection model for securing palmprint images in transmission or storage scenarios, called Dynamic Random Invisible Watermark Embedding (DRIWE) model. The DRIWE model implicitly embeds a watermark in each original palmprint ROI image, and then separates the embedded watermark from the watermarked image before identification. If the separated watermark is consistent with the original watermark, it indicates that the image is trustworthy and can be used in the subsequent recognition process. Otherwise, it proves that the image has been illegally tampered with. Furthermore, a two-dimensional image information entropy loss is proposed to enhance the generalization of the model to different watermarks. It ensures that the model can always assign enough information to the host image (i.e., original palmprint image) when different watermarks are applied. Thus, it enables the separator to extract the complete watermark from the watermarked image. This greatly enhances the dynamics and randomness of the watermark embedding process and further improves the ability to secure the data. Adequate experiments are conducted on two benchmark palmprint databases. The results show that the proposed DRIWE model has satisfactory attack resistance and strong generalization ability: even if only one watermark is used in training stage, it can be generalized to a dozen of other new watermark images in the testing stage. In addition, the optimal accuracy of the watermarked data is only reduced by 0.07% compared with the original data. 

Data Protection in Palmprint Recognition via Dynamic Random Invisible Watermark Embedding

With the wide application of biometrics technology, the scale of biometrics databases is increasing rapidly. In this situation, fast retrieval technology is more and more necessary for large-scale biometrics retrieval and recognition. Palmprint recognition is one of the emerging biometrics technologies. However, the research on fast palmprint retrieval algorithm is still preliminary. Hashing is one of the most popular image retrieval technologies due to its fast speed and low storage cost. In this paper, we propose a new deep palmprint hashing method, which integrates classification loss, pairing loss and quantization loss in a unified deep learning framework. Experimental results show that the proposed deep multi-loss hashing method has better performance for palmprint recognition and retrieval than other existing classic hashing methods.

Deep Multi-loss Hashing Network for Palmprint Retrieval and Recognition

Existing hand gesture authentication methods require the probe gesture types to be consistent with the registered ones, which reduces the user-friendliness and efficiency of authentication. In this paper, a video understanding based random hand gesture authentication method is introduced to eliminate this limitation, in which users only need to improvise a random hand gesture in front of an RGB camera without memory and hesitation in both the enrollment and verification stage. The random hand gesture is a promising biometric trait containing both physiological and behavioral characteristics. To fully unleash the potential of random hand gesture authentication, we design a simple but effective behavior representation (modality), temporal difference map, for better behavioral characteristic understanding, and present an efficient model called 3D Temporal Difference Symbiotic Neural Network (3DTDS-Net) that can separately extract physiological and behavioral features as well as automatically assign fusion weights for the two features to complement each other’s strengths based on the magnitude of behavioral features in an end-to-end fashion. We also adapt and reimplement 17 SOTA neural networks for authentication from other tasks, such as action classification and gait recognition, to make convincing comparisons. Extensive experiments on the SCUT-DHGA dataset demonstrate the effectiveness of temporal difference maps and the superiority of 3DTDS-Net. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SCUT-BIP-Lab/3DTDS-Net</uri> . 

Video Understanding-Based Random Hand Gesture Authentication

The usage of touchless palmprint recognition (PPR) increases due to its acceptant interaction mode. However, the region of interest (ROI) localization at a distance is quite challenging for touchless PPR in real-world scenarios with complex backgrounds and hand poses. To address this issue, we proposed a palm keypoint localization neural network (PKLNet) that combines information on the hand region, palm boundary, and finger valley edges to achieve accurate and robust keypoint localization. First, a two-stage neural network was proposed. It effectively adopted the transformer framework to capture global relations of the palm boundary points to perform palm region segmentation and ROI keypoint coordinate regression. Second, an image synthesis-based training strategy was developed based on conventional palmprint ROI localization methods. The obtained ROI localizer (namely PalmKit) can automatically generate palm region masks and keypoint coordinates, which can significantly simplify the data annotation process and hence liberate the heavy manual labor. Finally, extensive experiments were performed on various touchless palmprint datasets. The proposed PKLNet obtained an 82.1% success rate and a 7.7 pixels median localization error in the cross-dataset test. The results demonstrate that the proposed PKLNet is robust to palm rotation, translation, and inference from complex backgrounds, ensuring the usability of the touchless PPR technique in real-world application scenarios. 

PKLNet: Keypoint Localization Neural Network for Touchless Palmprint Recognition Based on Edge-Aware Regression

Existing hand gesture authentication methods require the probe gesture types to be consistent with the registered ones, which reduces the user-friendliness and efficiency of authentication. In this paper, a video understanding based random hand gesture authentication method is introduced to eliminate this limitation, in which users only need to improvise a random hand gesture in front of an RGB camera without memory and hesitation in both the enrollment and verification stage. The random hand gesture is a promising biometric trait containing both physiological and behavioral characteristics. To fully unleash the potential of random hand gesture authentication, we design a simple but effective behavior representation (modality), temporal difference map, for better behavioral characteristic understanding, and present an efficient model called 3D Temporal Difference Symbiotic Neural Network (3DTDS-Net) that can separately extract physiological and behavioral features as well as automatically assign fusion weights for the two features to complement each other’s strengths based on the magnitude of behavioral features in an end-to-end fashion. We also adapt and reimplement 17 SOTA neural networks for authentication from other tasks, such as action classification and gait recognition, to make convincing comparisons. Extensive experiments on the SCUT-DHGA dataset demonstrate the effectiveness of temporal difference maps and the superiority of 3DTDS-Net. The code is available at https://github.com/SCUT-BIP-Lab/3DTDS-Net.

Biometrics is one of the most effective solutions for personal authentication. With the wide use of biometrics technology, the scale of the biometrics database is increasing continuously. In this situation, fast matching and retrieval techniques become more and more necessary for large-scale biometrics recognition. Recently, palmprint recognition has become one of emerging biometrics technologies. Both low-resolution palmprint recognition and 3D palmprint recognition technologies were proposed and can be used for many civilian applications. However, the research on fast low-resolution palmprint and 3D palmprint retrieval is very preliminary. Hashing is one of the most popular techniques for image search due to its fast-computational speed and low storage cost. In this paper, we conduct a performance evaluation of hashing techniques for 2D and 3D palmprint retrieval and recognition. Several representative hashing methods are selected for performance evaluation including four unsupervised hashing methods, four supervised hashing methods, and four deep hashing methods. Experimental results demonstrate that some hashing methods such as column sampling based discrete supervised hashing (COSDISH) can obtain promising results.

A Performance Evaluation of Hashing Techniques for 2D and 3D Palmprint Retrieval and Recognition

Rapid assembly modeling system is a tool that needs to be used in the design process. Because the three-dimensional modeling is intuitive, powerful and can be used in actual engineering, it is more and more used in manufacturing production. In the rapid development of computer-aided design technology, CAD/CAM software is the most commonly used and most widely used modeling tool. Therefore, the purpose of this article to study the rapid assembly modeling in the computer-aided design system is to improve the performance and accuracy of the assembly system and promote the high-quality production of products. This article mainly uses experimental method and case analysis method to test the assembly system designed in this article. The experimental results show that, under low temperature conditions, the relative error and absolute error of the assembly size are in a small space, which meets the actual requirements. Therefore, the system designed in this paper can be used in practice.

https://doi.org/10.1088/1742-6596/2146/1/012022

Rapid Assembly Modeling in Computer Aided Design System

To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization. It provides a rough temporal location for an action but implicitly overstates the supervision from the annotated-frame during training, leading to the confusion between actions and backgrounds, i.e., action incompleteness and background false positives. To tackle the two challenges, in this work, we present the Snippet Classification model and the Dilation-Erosion module. In the Dilation-Erosion module, we expand the potential action segments with a loose criterion to alleviate the problem of action incompleteness and then remove the background from the potential action segments to alleviate the problem of action incompleteness. Relying on the single-frame annotation and the output of the snippet classification, the Dilation-Erosion module mines pseudo snippet-level ground-truth, hard backgrounds and evident backgrounds, which in turn further trains the Snippet Classification model. It forms a cyclic dependency. Furthermore, we propose a new embedding loss to aggregate the features of action instances with the same label and separate the features of actions from backgrounds. Experiments on THUMOS14 and ActivityNet 1.2 validate the effectiveness of the proposed method. Code has been made publicly available ( https://github.com/LingJun123/single-frame-TAL ).

pdf/dilation-erosion-for-single-frame-supervised-temporal-action-22m2bojz.pdf

Dilation-erosion for single-frame supervised temporal action localization

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Deep Multi-loss Hashing Network for Palmprint Retrieval and Recognition

A Performance Evaluation of Hashing Techniques for 2D and 3D Palmprint Retrieval and Recognition

Rapid Assembly Modeling in Computer Aided Design System

Dilation-erosion for single-frame supervised temporal action localization