Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network

Palmprint has attracted increasing attention due to its several advantages in the biometrics field. Deep learning has achieved remarkable performance in the computer vision area, so a large number of deep-learning-based methods have been proposed by the research community for palmprint recognition. The outputs of a deep hashing network (DHN) can be represented as a binary bit string, so DHN can reduce the storage and accelerate the matching/retrieval speed. In this paper, DHN is employed to extract the binary template for palmprint and palmvein verification. Spatial transformer network is used to overcome the rotation and dislocation. Palmprint and palmvein can be acquired from visible-light spectrums, including red (R), green (G), blue (B), and near infrared (NIR) spectrum, respectively. Since the features in different spectrums are different, their complementary advantages can be exploited to the full by fusion. Image-level fusion and score-level fusion are developed for palmprint-palmvein fusion recognition. The experiments demonstrate that score-level fusion can improve the accuracy efficiently.