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

Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

2016 ◽  
Vol 6 (6) ◽  
pp. 3255 ◽  
Author(s):  
Muhammad Kusban ◽  
Adhi Susanto ◽  
Oyas Wahyunggoro
Keyword(s):  
Dimension Reduction ◽  
Palmprint Recognition ◽  
Kernel Pca ◽  
Biometric Recognition ◽  
Palmprint Identification ◽  
Enhancement Method ◽  
Pca Method ◽  
Feature Information ◽  
Verification Process ◽  
Performance Rate

<p>Palmprint identification is part of biometric recognition, which attracted many researchers, especially when fusion with face identification that will be applied in the airport to hasten knowing individual identity. To accelerate the process of verification feature palms, dimension reduction method is the dominant technique to extract the feature information of palms.The mechanism will boost if the ROI images are processed prior to get normalize image enhancement.In this paper with three sample input database, a kernel PCA method used as a dimension reduction compared with three others and a skeleton filter used as a image enhancement method compared with six others. The final results show that the proposed method successfully achieve the target in terms of the processing time of $ 0.7415 $ second, the EER performance rate of 0.19 % and the success of verification process about 99,82 %.</p>

scholarly journals Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

2016 ◽  
Vol 6 (6) ◽  
pp. 3255
Author(s):  
Muhammad Kusban ◽  
Adhi Susanto ◽  
Oyas Wahyunggoro
Keyword(s):  
Dimension Reduction ◽  
Palmprint Recognition ◽  
Kernel Pca ◽  
Biometric Recognition ◽  
Palmprint Identification ◽  
Enhancement Method ◽  
Pca Method ◽  
Feature Information ◽  
Verification Process ◽  
Performance Rate

<p>Palmprint identification is part of biometric recognition, which attracted many researchers, especially when fusion with face identification that will be applied in the airport to hasten knowing individual identity. To accelerate the process of verification feature palms, dimension reduction method is the dominant technique to extract the feature information of palms.The mechanism will boost if the ROI images are processed prior to get normalize image enhancement.In this paper with three sample input database, a kernel PCA method used as a dimension reduction compared with three others and a skeleton filter used as a image enhancement method compared with six others. The final results show that the proposed method successfully achieve the target in terms of the processing time of $ 0.7415 $ second, the EER performance rate of 0.19 % and the success of verification process about 99,82 %.</p>

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
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