Efficient Deep Palmprint Recognition via Distilled Hashing Coding

Shao, Huikai; Zhong, Dexing; Du, Xuefeng

doi:10.1109/cvprw.2019.00098

Cited by 46 publications

(33 citation statements)

References 36 publications

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“…Then, what we need to do is scale normalization, which means to determine the side length of the ROI. The work reported in [10,17,21,30,33,47] utilized the palm width to determine the size of the ROI, while the work reported in [4,[27][28][29] utilized the length of the tangent line to determine the size of the ROI. In [30], the author found that big ROI performs better.…”

Section: Classical Methodsmentioning

confidence: 99%

“…However, it is not sufficient and it leads the algorithms being sensitive to palm postures and background objects. In recent years, many new methods have been proposed, such as the active shape model (ASM)-based methods [48,49], the active appearance model (AAM)-based methods [17,29,50], the regression tree-based methods [47], and the deep learning-based methods [24,41]. The new-generation methods utilized both the edge and texture information to learn much more robust models to regress the landmarks.…”

Section: New-generation Methodsmentioning

confidence: 99%

“…In [47], at first the palm position is detected by techniques of sliding window, histogram of oriented gradient (HOG) [53], and support vector machine (SVM). In the training set, 14 landmark points are determined and labeled manually.…”

Section: New-generation Methodsmentioning

confidence: 99%

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Region of Interest Localization Methods for Publicly Available Palmprint Databases

Xu¹,

Fan²,

Li³

et al. 2021

Biometric Systems

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So far, there exist many publicly available palmprint databases. However, not all of them have provided the corresponding region of interest (ROI) images. If everyone uses their own extracted ROI images for performance testing, the final accuracy is not strictly comparable. Since ROI localization is the critical stage of palmprint recognition. The location precision has a significant impact on the final recognition accuracy, especially in unconstrained scenarios. This problem has limited the applications of palmprint recognition. However, many currently published surveys only focus on feature extraction and classification methods. Throughout these years, many new ROI localization methods have been proposed. In this chapter, we will group the existing ROI localization methods into different categories, analyze their basic ideas, reproduce some of the codes, make comparisons of their performances, and provide further directions. We hope this could be a useful reference for further research.

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Section: Classical Methodsmentioning

confidence: 99%

Section: New-generation Methodsmentioning

confidence: 99%

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Region of Interest Localization Methods for Publicly Available Palmprint Databases

Xu¹,

Fan²,

Li³

et al. 2021

Biometric Systems

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“…PolyU multispectral palmprint database [6 ] and Xi'an Jiaotong University Unconstrained Palmprint (XJTU‐UP) database [7 ] are adopted to evaluate FHL. PolyU multispectral database is collected under four spectral bands, i.e.…”

Section: Databasesmentioning

confidence: 99%

“…indoor natural and flashlights. So there are ten palmprint sub‐datasets, denoted as IF, IN, SF, SN, MF, MN, HF, HN, LF, and LN, like [7 ]. Each sub‐dataset contains 2000 images from 200 categories.…”

Section: Databasesmentioning

confidence: 99%

Towards privacy palmprint recognition via federated hash learning

Shao

Zhong

2020

Electron. lett.

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Nowadays, deep learning‐based palmprint recognition methods have achieved great success. However, they are mainly focused on the accuracy and ignore the privacy, which is more important in the practical applications. In this Letter, a novel method, federated hash learning (FHL), is proposed for privacy palmprint recognition. There are several agents deploy in different communities, and they have different models and private data. An available public dataset is introduced to provide communications for each agent. Through appropriate federated loss, the agents are connected to help each other train the models to improve the accuracy. Experiments are conducted on constrained and unconstrained palmprint benchmarks. The results demonstrate that the authors’ FHL can outperform other baselines and obtain promising accuracy.

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