Hand Based Biometric Identification using Energy Compaction in Transform Domain including New Multi resolution Hybrid Wavelet

Abstract

The hand-based biometrics viz. fingerprint, palmprint, finger-knuckle print etc. are the most popular among all the biometrics currently in use. The main advantages of using hand-based biometrics are user acceptability, ease of acquisition and plenteous, stable features. Hand-based biometric identification systems have enormous and ever-increasing applications. From electronic banking to airport security and from forensics to user identification cards, these hand-based biometrics find applications in all arenas. A Hand-based biometric identification system recognizes an individual by matching the features of its biometric with the enrolled templates in the database. newlineIn this work, three hand-based biometrics viz. fingerprint, palmprint and finger-knuckle print have been used for identification. The biometric trait is supposed to be unique for each human being. This uniqueness needs to be extracted in the form of features and used for identification. Feature extraction is performed in both enrollment and identification phases. The features of each of the three hand-based biometrics have been extracted using four main approaches. All the four approaches are in transform domain and use the energy compaction property of transforms, which has been used in a unique way to extract the features. newline