Integrated Biometrics (Face, Fingerprint, Iris, Palmprint and Ear) using PCA

Integrated Biometrics is a rising domain in biometric technology where more than one biometric trait is combined to improve the performance. Integrated biometrics has much higher efficiency than unimodal biometrics. Integrating the biometric system results in enlarge the user database but increase the level of security. In this paper, a study of integrated biometrics has been done with five biometrics considered face, iris, ear, palm and finger respectively. In biometric system, Problems arise when integrated performing recognition in a high-dimensional space. Signi�cant improvements can be achieved by mapping the data into a lower dimensionality space. This can be done by PCA approach. PCA method reduces memory requirement of the system and computation time. This paper presents a method for face, iris, fingerprint, palm print and ear recognition based on Principal Component Analysis (PCA). I have applied the same PCA algorithm for all these biometric traits.