Detection of spoofing attacks for ear biometrics through image quality assessment and deep learning

Abstract

Ear recognition systems are one of the popular person identification systems. These biometric systems need to be protected against attackers. In this paper, a novel method is proposed to detect spoof attacks within ear recognition systems. The proposed method employs Convolutional Neural Network (CNN) which is based on deep learning and Image Quality Measure (IQM) techniques to detect printed photo attacks against ear recognition systems. Full-reference and no-reference image quality measures are used to extract ear image features. Score-level fusion is used to combine the scores obtained from image quality measures. Finally, decision-level fusion is employed to fuse the decisions obtained from CNN and IQM systems. The final decision is obtained as real or fake image as the output of the whole system. The experiments are conducted on publicly available ear datasets namely, AMI, UBEAR, IITD, USTB set 1 and USTB set 2 and the obtained results are compared with the state-of-the-art methods that are focused on printed photo attacks as well.