Key Point Localization Based on Intersecting Circle for Palmprint
                    Preprocessing in Public Security

Zhou, Zhifeng; Chen, Qi; Leng, Lu

doi:10.33530/jdaat.2019.1.2.24

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…A judicious data selection was undertaken given the subtle variations within hand images of single datasets and the imperative to conduct experiments under cross-dataset conditions. Specifically, 200 images were randomly sampled from each dataset for experimentation purposes, and all 200 images sampled from each dataset were from different hands, except for the 200 images sourced from Zhou [32], which were from 160 hands.…”

Section: Methodsmentioning

confidence: 99%

A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack

Yan,

Leng,

Teoh

et al. 2024

Applied Sciences

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Palmprint recognition (PPR) has recently garnered attention due to its robustness and accuracy. Many PPR methods rely on preprocessing the region of interest (ROI). However, the emergence of ROI attacks capable of generating synthetic ROI images poses a significant threat to PPR systems. Despite this, ROI attacks are less practical since PPR systems typically take hand images as input rather than just the ROI. Therefore, there is a pressing need for a method that specifically targets the system by composing hand images. The intuitive approach involves embedding an ROI into a hand image, a comparatively simpler process requiring less data than generating entirely synthetic images. However, embedding faces challenges, as the composited hand image must maintain a consistent color and texture. To overcome these challenges, we propose a training-free, end-to-end hand image composition method incorporating ROI harmonization and palm blending. The ROI harmonization process iteratively adjusts the ROI to seamlessly integrate with the hand using a modified style transfer method. Simultaneously, palm blending employs a pretrained inpainting model to composite a hand image with a continuous transition. Our results demonstrate that the proposed method achieves a high attack performance on the IITD and Tongji datasets, with the composited hand images exhibiting realistic visual quality.

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Section: Methodsmentioning

confidence: 99%

A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack

Yan,

Leng,

Teoh

et al. 2024

Applied Sciences

Self Cite

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“…The finger valley points were then determined with a horizontal/vertical line (depending on the orientation of the hand), having 8 points of transition from non-hand region to hand region. Zhou et al [61] similarly filtered the hand contour points using intersecting circles. By evaluating the intersection of these circles with the other hand contour points and the angles they made, the finger valley points were determined.…”

Section: A Standard Palmprint Roi Extractionmentioning

confidence: 99%

Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

2020

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An unconstrained palmprint region of interest extraction method based on lightweight networks

Lin,

Chen,

Zou

et al. 2024

PLoS ONE

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Accurately extracting the Region of Interest (ROI) of a palm print was crucial for subsequent palm print recognition. However, under unconstrained environmental conditions, the user’s palm posture and angle, as well as the background and lighting of the environment, were not controlled, making the extraction of the ROI of palm print a major challenge. In existing research methods, traditional ROI extraction methods relied on image segmentation and were difficult to apply to multiple datasets simultaneously under the aforementioned interference. However, deep learning-based methods typically did not consider the computational cost of the model and were difficult to apply to embedded devices. This article proposed a palm print ROI extraction method based on lightweight networks. Firstly, the YOLOv5-lite network was used to detect and preliminarily locate the palm, in order to eliminate most of the interference from complex backgrounds. Then, an improved UNet was used for keypoints detection. This network model reduced the number of parameters compared to the original UNet model, improved network performance, and accelerated network convergence. The output of this model combined Gaussian heatmap regression and direct regression and proposed a joint loss function based on JS loss and L2 loss for supervision. During the experiment, a mixed database consisting of 5 databases was used to meet the needs of practical applications. The results showed that the proposed method achieved an accuracy of 98.3% on the database, with an average detection time of only 28ms on the GPU, which was superior to other mainstream lightweight networks, and the model size was only 831k. In the open-set test, with a success rate of 93.4%, an average detection time of 5.95ms on the GPU, it was far ahead of the latest palm print ROI extraction algorithm and could be applied in practice.

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Key Point Localization Based on Intersecting Circle for Palmprint Preprocessing in Public Security

Cited by 3 publications

References 33 publications

A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack

A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack

Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

An unconstrained palmprint region of interest extraction method based on lightweight networks

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