Advanced Biometrics with Deep Learning

Dear Colleagues,

Biometrics such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition etc. as a means of identity management has become commonplace nowadays for various applications. Biometric systems follow a typical pipeline that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction and recognition based solely on biometric data. The objective of this Special Issue is to invite high-quality, state-of-the-art research papers that deal with challenging issues in advanced deep learning-based biometric systems. We solicit the original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal. Topics of interest include, but are not limited to:

• Biometric signal, image and video preprocessing based on deep learning
• Biometric feature extraction based on deep learning
• Biometric classification and recognition based on deep learning
• End-to-end deep learning for biometric systems
• Multimodal biometrics based on deep learning
• Spoof detection based on deep learning
• Biometric encryption and template protection based on deep learning.

Prof. Dr. Andrew Teoh Beng Jin
Prof. Dr. Lu Leng
Guest Editors

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