Person Identification with Voice and Ear-print in Degraded Conditions for Smart Home Access

Since the smartphone is adapted to the ambient intelligence and the smart home systems are remotely accessed through the smartphones, there is a need for a secure authentication system based on some biometrics proprieties that can be taken from a smartphone. The identification of persons through ear and voice print is one of the basic biometric matters. The earlier research in ear recognition have shown that human ear is one of the representative human biometrics with uniqueness and stability. Indeed, the human voice is a perfect source of data for person identification in many applications. In this paper, we propose a fusion between the ear and voice biometrics in degraded conditions in a smart home context at 3 levels (feature, score, and decision). The experiments are conducted on the EVDDC database and a chimeric database (TIMIT and USTB-I). The best results are obtained with the feature level fusion (95.8%) with the KNN classifier.