Deep learning feature extraction for multispectral palmprint identification
| Autor: | Djamel Samai, Fatima Zohra Laallam, Abdelah Meraoumia, Khaled Bensid |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Biometrics
Computer science business.industry Deep learning 020208 electrical & electronic engineering Feature extraction Multispectral image System identification Pattern recognition 02 engineering and technology Atomic and Molecular Physics and Optics Computer Science Applications Identification (information) Wavelet 0202 electrical engineering electronic engineering information engineering Discrete cosine transform 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | Journal of Electronic Imaging. 27:1 |
| ISSN: | 1017-9909 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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