PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognition

Abstract

With the development of high-resolution fingerprint scanners, high-resolution fingerprint-based biometric recognition has received increasing attention in recent years. This paper presents a pore feature-based approach for biometric recognition. Our approach employs a convolutional neural network (CNN) model, DeepResPore, to detect pores in the input fingerprint image. Thereafter, a CNN-based descriptor is computed for a patch around each detected pore. Specifically, we have designed a residual learning-based CNN, referred to as PoreNet that learns distinctive feature representation from pore patches. For verification, the match score is generated by comparing pore descriptors obtained from a pair of fingerprint images in bi-directional manner using the Euclidean distance. The proposed approach for high-resolution fingerprint recognition achieves 2.91% and 0.57% equal error rates (EERs) on partial (DBI) and complete (DBII) fingerprints of the benchmark PolyU HRF dataset. Most importantly, it achieves lower FMR1000 and FMR10000 values than the current state-of-the-art approach on both the datasets.

7 pages, 4 figures, 6tables

References in corpus (8)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• FaceNet: A Unified Embedding for Face Recognition and Clustering
• TensorFlow: A system for large-scale machine learning
• Unsupervised Domain Adaptation by Backpropagation
• Working hard to know your neighbor's margins: Local descriptor learning loss
• Pore detection in high-resolution fingerprint images using Deep Residual Network
• Automatic Dataset Annotation to Learn CNN Pore Description for Fingerprint Recognition