Sparse representation of finger knuckle print images for personal identification

Abstract

Fraud keeps increasing in our society and security applications become crucial and needed in our daily life. Biometric technology attempts to stop fraud and falsification in different opportunities such as bank services, access to controlled areas or crossing frontiers, by recognizing the identity of a person using his physiological (fingerprint, iris, face) or behavioral modalities (gait, signature). In this article, we focus on an emerging biometric modality called the finger knuckle print (FKP). In fact, this modality has several advantages such as the easy distinction between different persons, stability over time and user acceptance. So, an FKP identification approach is proposed using scale invariant feature transform descriptors based sparse representation method. The classification step, between training and testing FKP samples, is made using the support vector machines method. Experiments applied on two public FKP databases: The Hong Kong Polytechnic University (PolyU) Contactless Finger Knuckle Images Database and the Indian Institute of Technology Delhi (IITD) Finger Knuckle Database, containing respectively 2500 and 790 images, demonstrate high correct identification rates by reaching 98.58% and 99.15% for these two databases.