Learning Multilayer Feature Projection for Homogeneous and Heterogeneous Palmprint Recognition

Owing to its remarkable convenience, weak invasiveness, and strong private security, palmprint recognition has become one of the most promising biometric methods and has attracted increasing attention in both academia and industry. Although considerable recognition performance has been achieved by existing palmprint learning methods, they generally require the use of substantial labeled datasets and involve substantial computational overhead for feature learning. In this article, we propose a novel multilayer projection learning (MLPL) method to achieve efficient palmprint feature learning and recognition. First, we transform the palmprint images into their direction-specific representations by computing the difference in the multiple directional responses. Then, we learn three layers of feature projections for robust feature learning, including low-rank projection for image noise decoupling, feature projection for discriminative feature exploration, and quantization projection for information preservation during feature encoding. With multilayer feature projections, palmprint images can be transformed into discriminative feature representations through a single-step process for efficient palmprint recognition. Moreover, we extend the proposed MLPL, referred to as E-MLPL, by minimizing the representation discrepancy between heterogeneous palmprint images to make it applicable for heterogeneous palmprint recognition. The results obtained from five widely adopted databases confirm the superior performance of the proposed method in terms of both accuracy and efficiency.