Downsampling in uniformly-spaced windows for coding-based Palmprint recognition

Yang, Ziyuan; Leng, Lu; Min, Weidong

doi:10.1007/s11042-023-14574-z

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Leng et al [13] selected the best impact pixel from the 16 candidates in each block. Yang et al [14] conducted the downsampling in uniformly-spaced windows, which avoided the small distance and strong correlation between the adjacent preserved pixels.…”

Section: Local Texture Coding Methodsmentioning

confidence: 99%

Double Quantification of Template and Network for Palmprint Recognition

2023

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The outputs of deep hash network (DHN) are binary codes, so DHN has high retrieval efficiency in matching phase and can be used for high-speed palmprint recognition, which is a promising biometric modality. In this paper, the templates and network parameters are both quantized for fast and light-weight palmprint recognition. The parameters of DHN are binarized to compress the network weight and accelerate the speed. To avoid accuracy degradation caused by quantization, mutual information is leveraged to optimize the ambiguity in Hamming space to obtain a tri-valued hash code as a palmprint template. Kleene Logic’s tri-valued Hamming distance measures the dissimilarity between palmprint templates. The ablation experiments are tested on the binarization of the network parameter, and the normalization and trivialization of the deep hash output value. The sufficient experiments conducted on several contact and contactless palmprint datasets confirm the multiple advantages of our method.

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Section: Local Texture Coding Methodsmentioning

confidence: 99%

Double Quantification of Template and Network for Palmprint Recognition

2023

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“…In this field, the palmprint is considered a new modality, a unique entity that is stable over time and has a rich information structure [5,6] . For palmprint recognition, many effective methods have been proposed [7][8][9][10][11] . Among all kinds of methods, HOG [12] is a powerful descriptor that has been exploited for biometrics such as face, gait, and palmprint recognition in recent years.…”

Section: Introductionmentioning

confidence: 99%

Enhanced histogram of oriented lines for palmprint recognition

Tiantian,

Wei,

Yang

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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Recently, two new emerging biometrics technologies, 2D low-resolution palmprint recognition technologies, have received wide attention. Numerous methods have been developed and proposed for palmprint recognition. Among them, one local descriptor, histogram of oriented lines (HOL), has achieved very desirable performance. However, HOL is constructed on a single scale and only exploits the orientation of the minimum response, and thus may lose some important information. In this paper, aiming to further increase the discriminative power of HOL, we propose an enhanced HOL (EHOL) descriptor jointly exploring the information in multiscale and multi-orientation maps for robust palmprint recognition. Furthermore, the EHOL descriptor is only constructed by orientation information and does not use histogram normalization and block strategies to generate the final histograms. Thorough experiments are conducted on two palmprint databases. The experimental results demonstrate that the recognition performance of the proposed EHOL is obviously better than that of state-of-the-art methods.

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Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics

Chen,

Leng,

Yang

et al. 2024

Int. J. Neur. Syst.

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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.

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Downsampling in uniformly-spaced windows for coding-based Palmprint recognition

Cited by 3 publications

References 35 publications

Double Quantification of Template and Network for Palmprint Recognition

Double Quantification of Template and Network for Palmprint Recognition

Enhanced histogram of oriented lines for palmprint recognition

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics

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