Extreme Downsampling and Joint Feature for Coding-based Palmprint Recognition

2020 ◽  
pp. 1-1
Author(s):  
Ziyuan Yang ◽  
Lu Leng ◽  
Weidong Min
Keyword(s):  
Palmprint Recognition

scholarly journals 2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

2021 ◽  
Author(s):  
Wei Jia ◽  
Wei Xia ◽  
Yang Zhao ◽  
Hai Min ◽  
Yan-Xiang Chen
Keyword(s):  
Deep Learning ◽  
Recognition Performance ◽  
Research Direction ◽  
Palmprint Recognition ◽  
Neural Architecture ◽  
Development Direction ◽  
Vein Recognition ◽  
Palm Vein ◽  
2D And 3D ◽  
Important Research Direction

AbstractPalmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

Jointly Learning Multiple Curvature Descriptor for 3D Palmprint Recognition

2021 ◽  
Author(s):  
Lunke Fei ◽  
Jianyang Qin ◽  
Peng Liu ◽  
Jie Wen ◽  
Chunwei Tian ◽  
...  
Keyword(s):  
Palmprint Recognition

Palm region extraction for contactless palmprint recognition

2015 ◽  
Author(s):  
Koichi Ito ◽  
Takuto Sato ◽  
Shoichiro Aoyama ◽  
Shuji Sakai ◽  
Shusaku Yusa ◽  
...  
Keyword(s):  
Palmprint Recognition ◽  
Region Extraction

Gabor-based kernel PCA for palmprint recognition

2007 ◽  
Vol 43 (20) ◽  
pp. 1077 ◽  
Author(s):  
M. Ekinci ◽  
M. Aykut
Keyword(s):  
Palmprint Recognition ◽  
Kernel Pca

A Palmprint Recognition Approach Based on Image Segmentation of Region of Interest

2016 ◽  
Vol 30 (02) ◽  
pp. 1656002 ◽  
Author(s):  
Qing E Wu ◽  
Zhiwu Chen ◽  
Ruijie Han ◽  
Cunxiang Yang ◽  
Yuhao Du ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Palmprint Recognition ◽  
Characteristic Points ◽  
Positioning Method ◽  
Roi Extraction ◽  
Energy Feature

To carry out an effective recognition for palmprint, this paper presents an algorithm of image segmentation of region of interest (ROI), extracts the ROI of a palmprint image and studies the composing features of palmprint. This paper constructs a coordinate by making use of characteristic points in the palm geometric contour, improves the algorithm of ROI extraction and provides a positioning method of ROI. Moreover, this paper uses the wavelet transform to divide up ROI, extracts the energy feature of wavelet, gives an approach of matching and recognition to improve the correctness and efficiency of existing main recognition approaches, and compares it with existing main approaches of palmprint recognition by experiments. The experiment results show that the approach in this paper has the better recognition effect, the faster matching speed, and the higher recognition rate which is improved averagely by 2.69% than those of the main recognition approaches.

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