Finger-Vein Recognition Based on an Enhanced HMAX Model

To overcome the shortcomings of the traditional methods, in this paper, we investigate the role of a biologically-inspired network for finger-vein recognition. Firstly, robust feature representation of finger-vein images are obtained from an enhanced Hierarchical and X (HMAX) model, and successively class by the extreme learning machine (ELM). The enhanced HMAX model could calculate complex feature representations by the way of simulating the hierarchical processing mechanism in primate visual cortex. ELM performs well in classification while keeping a faster learning speed. Our proposed method is tested on the MMCBNU-6000 dataset, and achieved good performances compared with state-of-the-art methods. The results further the case for biologically-motivated approaches for finger-vein recognition.