A robust approach for palmprint biometric recognition

Abstract

Biometrics system uses an individual's physical or behavioural feature to recognise an individual. An easy-to-capture biometric modality that could work well with a commodity camera is palmprint. It has coarse lines which can be easily detected using a low resolution camera. To achieve superior recognition results, an accurate segmentation of region of interest is very crucial. In this work, a novel palmprint ROI extraction algorithm has been presented which extracts a fixed size region from a full hand image. The proposed approach segments the region of interest which is invariant to the angle between the fingers. Firstly, we detect the palm region and segment it from full hand image and mark it as ROI. After the ROI extraction, the features are extracted by fusing the BSIF and BRISK features. Finally, the classification is performed by sparse representation classifier (SRC). We have validated the proposed approach on dataset which contains various images of hand at different angle between the fingers. The proposed method had successfully resolved the issues of ROI extraction at different angle between the fingers, and experimental results shows that the proposed approach has successfully achieved the accuracy of 90%.