Advanced Biometrics with Deep Learning

Biometrics such as fingerprint iris face hand print hand vein speech and gait recognition etc. as a means of identity management have 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. This Special Issue has collected 12 high-quality state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely face biometrics medical electronic signals (EEG and ECG) voice print and others.