余弦相似度保持的掌纹识别算法

doi:10.12052/gdutxb.210023

Abstract

Abstract: Palmprint recognition, as a promising biometric recognition technology, has the advantages of high recognition accuracy and stable characteristics. Conventional hand-craft based palmprint recognition methods extract the principal lines and minutiae points depending on prior knowledge, which suffer from various limitations, such as the difficulties of scalability, feature extraction and information learning. To address this, a learning-based palmprint recognition method is proposed. Specifically, the Pixel Different Feature (PDV) feature is first extracted from palmprint image. Then, the binary representation and projection function based on PDV features is simultaneously learned via a cosine similarity preserving model. To alleviate the effects of noise such as palmprint position shifting and illumination changing, the binary representation is finally converted into a histogram descriptor. The experimental results on three widely used palmprint databases demonstrate that the proposed method can better excavate the intrinsic structure of palmprint images and significantly improve the performance of palmprint recognition.

Key words: computer image processing, biometric recognition, palmprint recognition, feature extraction

CLC Number:

TP391.4

Qiu Zhan-chun, Fei Lun-ke, Teng Shao-hua, Zhang Wei. Palmprint Recognition Based on Cosine Similarity[J].Journal of Guangdong University of Technology, 2022, 39(03): 55-62.

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