Consistency and label constrained transfer low-rank representation for cross-light finger vein recognition

Finger vein sensors are embedded into all kinds of electronic devices for personal identification, and the upgrading of sensors is unavoidable. Therefore, the concern about cross-sensor finger vein recognition is raised recently. However, little attention is paid to cross-sensor finger vein recognition. The imaging light variation is one main difference between different sensors, and it brings large image differences, seriously degrading finger vein recognition performance. This paper focuses on cross-light finger vein recognition problem, in which we assume that the training and testing finger vein images are captured by different near-infrared lights, and proposes a consistency and label constrained transfer low-rank representation (CLTLRR) method for dealing with cross-light finger vein recognition. In the proposed method, we first transfer cross-light finger vein images into a common feature space to narrow the gap between training images and testing images, and achieve the low-rank linear representations of images. Then, we develop a consistency constraint between the low-rank coefficients in the common feature space and the sparse coefficients in the original feature space to enhance the discrimination of linear representation. In addition, we design a class label constraint for the projection matrix to guide image transfer. Finally, the low-rank coefficients and the projected features in the common feature space are integrated for recognition. Experiments are performed on single-light and cross-light finger palmar vein databases and finger dorsal vein databases, and the experimental results prove the effectiveness of our CLTLRR.