1. Multispectral Palm Print and Palm Vein Acquisition Platform and Recognition Method Based on Convolutional Neural Network
- Author
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Wang Jishuai, Yang Renbing, Lei Wang, Pan Yujun, Qian Qing, Wenbo Cheng, and Qiang Zhang
- Subjects
0209 industrial biotechnology ,Palm vein ,Palm print ,General Computer Science ,Computer science ,business.industry ,Multispectral image ,Pattern recognition ,02 engineering and technology ,Convolutional neural network ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
Biometrics recognition takes advantage of feature extraction and pattern recognition to analyze the physical and behavioral characteristics of biological individuals to achieve the purpose of individual identification. As a typical biometric technology, palm print and palm vein have the characteristics of high recognition rate, stable features, easy location and good image quality, which have attracted the attention of researchers. This paper designs and develops a multispectral palm print and palm vein acquisition platform, which can quickly acquire palm spectrum and palm vein multispectral images with seven different wavelengths. We propose a multispectral palm print palmar vein recognition framework, and feature-level image fusion is performed after extracting features of palm print palmar vein images at different wavelengths. Through the multispectral palm print palm vein image fusion experiment, a more feasible multispectral palm print and palm vein image fusion scheme is proposed. Based on the results of image fusion, we further propose an improved convolutional neural network (CNN) for model training to achieve identity recognition based on multispectral palm print palm vein images. Finally, the effects of different CNN network structures and learning rates on the recognition results were analyzed and compared experimentally.
- Published
- 2021