Learning Discriminative Finger-knuckle-print Descriptor
Lunke Fei, Bob Zhang, Shaohua Teng, An Zeng, Chunwei Tian, Wei Zhang
Abstract
Direction information has been intensively investigated for Finger-Knuckle-Print (FKP) recognition. However, most existing direction-based KFP recognition methods are handcrafted, which are heuristic and require too much prior knowledge to engineer them. In this paper, we propose a discriminative direction binary feature learning (DDBFL) method for FKP recognition. We first propose a direction convolution difference vector (DCDV) to better describe the direction information of FKP images. Then, we learn a feature projection to convert the DCDV into binary codes, which are compact for the intra-class samples and more separable for the inter-class samples. Finally, we concatenate the block-wise histograms of the DDBFL codes to form the final descriptor for FKP recognition. Experimental results on the baseline PolyU FKP database demonstrate the competitive performance of the proposed method.
BibTeX
@inproceedings{icassp2019_learningdiscrimi,
title = {Learning Discriminative Finger-knuckle-print Descriptor},
author = {Lunke Fei and Bob Zhang and Shaohua Teng and An Zeng and Chunwei Tian and Wei Zhang},
booktitle = {ICASSP 2019},
year = {2019}
}