Deep learning feature extraction for multispectral palmprint identification

Khaled Bensid; Djamel Samai; Fatima Zohra Laallam; Abdelah Meraoumia

doi:10.1117/1.JEI.27.3.033018

22 May 2018 Deep learning feature extraction for multispectral palmprint identification

Khaled Bensid, Djamel Samai, Fatima Zohra Laallam, Abdelah Meraoumia

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 3, 033018 (May 2018). https://doi.org/10.1117/1.JEI.27.3.033018

Abstract

Person’s identity validation is becoming much more essential due to the increasing demand for high-security systems. A biometric system testifies the authenticity of specific physiological or behavioral characteristics-based biometric technology. This technology has been successfully applied to verification and identification systems. We analyze the multispectral palmprint biometric identification system in unimodal and multimodal modes. In an identification system, the feature extraction is a crucial step. For this reason, we propose an efficient deep learning feature extraction algorithm called discrete cosine transform network (DCTNet). The effectiveness of the proposed approach has been evaluated on two publicly available databases: CASIA and PolyU. The obtained results clearly indicate that the DCTNet deep learning-based feature extraction technique can achieve comparable performance to the best of the state-of-the-art techniques.

Citation Download Citation

Khaled Bensid, Djamel Samai, Fatima Zohra Laallam, and Abdelah Meraoumia "Deep learning feature extraction for multispectral palmprint identification," Journal of Electronic Imaging 27(3), 033018 (22 May 2018). https://doi.org/10.1117/1.JEI.27.3.033018

Received: 14 November 2017; Accepted: 30 April 2018; Published: 22 May 2018

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