Ear anti-spoofing against print attacks using three-level fusion of image quality measures

Abstract

Ear biometric recognition systems have been developed in the last decades. Consequently, the attacks to these biometrics security systems became inevitable. Recently, image quality assessment methods are one of the popular methods used for countering spoof attacks. There is no ear anti-spoofing system proposed in the literature. Therefore, in order to counter spoof attacks on ear biometric recognition systems, an efficient method is proposed in this study. This proposed method is based on full-reference and no-reference image quality assessment methods. Three-level score-level fusion and decision-level fusion techniques are employed in the solution of the proposed method. The experiments are conducted by preparing the printed photo attack images of AMI and UBEAR ear databases. The proposed system significantly recognizes real and fake ear images compared to the other systems implemented in this study. Half total error rates of the proposed system using printed photo attack images are compared with the error rates of the other implemented systems employing various fusion techniques for ear anti-spoofing. Additionally, the proposed system is compared with the state-of-the-art anti-spoofing systems and CNN-based deep learning anti-spoofing systems against print attacks on other biometric traits since this is the first study presenting ear anti-spoofing systems using score-level and decision-level fusion of image quality assessment methods.