Label-Guided Dictionary Pair Learning for ECG Biometric Recognition

Abstract

ECG biometric recognition has received plenty of attention in biometrics area. In recent years, various classical sparse representation and dictionary learning methods have been utilized in ECG biometric recognition. However, to produce better classification results, l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf> -norm is used to regularize the representation coefficients, which undoubtedly brings time cost problem. To overcome this limitation, our method, namely label-guided dictionary pair learning, aims to learn a projective dictionary and reconstructed dictionary jointly, which achieves signal representation and reconstruction simultaneously. Introduction of label information with each dictionary item and Fisher-like regularization on projective dictionary enforce discriminability during the dictionary learning process. Alternating direction method of multipliers is then exploited to optimize the corresponding objective function. Extensive experiments on two databases demonstrate that our method can achieve better performance compared with state-of-the-art ECG biometric recognition methods.