Multimodal Biometrics for Person Identification Using Ear and Palm Print Features

Abstract

ABSTRACT Biometrics are automated methods of recognising a person based on physiological or behavioural characteristics. To discriminate individuals, multimodal biometrics has already proven as an effective strategy. Biometric features can be broadly classified as physiological features and behavioural features. Ear, face, and palm come under physiological features. Gait and signature verification come under behavioural features. Combining multiple human trait features for biometric identification is multimodal biometric identification. Here, ear and palm print are the two biometric modalities used for person identification fused at feature level. To extract the features for person identification, Multiblock Local Binary Pattern and Binarised Statistical Image Features are used. Required intrusive means for acquiring the information can be a common drawback when using biometric features such as iris pattern, facial traits, etc. To overcome the drawbacks, ear can be used as a biometric feature; it also has an advantage of no changes over time and not influenced by facial expressions.