Fingerprint indexing based on pyramid deep convolutional feature

Abstract

The ridges of fingerprint contain enormous discriminative information for fingerprint indexing, however it is hard to depict the structure of ridges for rule-based methods because of nonlinear distortion. This paper investigates to represent the structure of ridges by Deep Convolutional Neural Network (DCNN). The indexing approach partitions the fingerprint image into increasing fine sub-region and extracts feature from each sub-region by DCNN, forming pyramid deep convolutional feature, to represent the global patterns and local details (especially minutiae). Extensive experimental results show that the proposed method achieves better performance on accuracy and efficiency than other prominent indexing approaches. Finally, occlusion sensitivity, visualization and fingerprint reconstruction techniques are employed to explore which attributes of ridges are described in deep convolutional feature.