掌纹识别研究进展综述

Palmprint images contain rich features and they can be easily combined with hand dorsal veins, finger-knuckle-prints and hand shapes to form multimodal features. Thus, palmprint recognition becomes a hot topic in the field of biometric recognition. In this paper, the basic process of palmprint recognition is discussed from three aspects: the collection of palmprint images, region of interest detection, and feature extraction and matching. Multimodal methods based on fusion of different features are also explored. Besides, on the basis of feature extraction means, palmprint recognition algorithms are roughly divided into hand-craft based algorithms including encoding, structure and statistics, and subspace and feature learning based algorithms including machine learning and deep learning. The algorithms are compared and analyzed in detail. Finally, challenges and future perspectives in palmprint recognition are discussed, especially palmprint recognition system in complex environment across different devices.