A multimodal liveness detection using statistical texture features and spatial analysis

Abstract

Biometric authentication can establish a person’s identity from their exclusive features. In general, biometric authentication can vulnerable to spoofing attacks. Spoofing referred to presentation attack to mislead the biometric sensor. An anti-spoofing method is able to automatically differentiate between real biometric traits presented to the sensor and synthetically produced artifacts containing a biometric trait. There is a great need for a software-based liveness detection method that can classify the fake and real biometric traits. In this paper, we have proposed a liveness detection method using fingerprint and iris. In this method, statistical texture features and spatial analysis of the fingerprint pattern is utilized for fake or real classification. The approach is further improved by fusing iris modality with the fingerprint modality. The standard Haralick’s statistical features based on the gray level co-occurrence matrix (GLCM) and Neighborhood Gray-Tone Difference Matrix (NGTDM) are used to generate a feature vector from the fingerprint. Texture feature from iris is used to boost the performance of the proposed liveness detection method. For the fusion Dempster-Shafer (D-S) approach is used at the decision level. Experiments have been performed on ATVS dataset and LivDet2011 dataset. The results show the convincing and effective outcomes of the proposed method.