Score Level Fusion of Ear and Face Local 3D Features for Fast and Expression-Invariant Human Recognition

Increasing risks of spoof attacks and other common problems of unimodal biometric systems such as intra-class variations, nonuniversality and noisy data necessitate the use of multimodal biometrics. The face and the ear are highly attractive biometric traits for combination because of their physiological structure and location. Besides, both of them can be acquired non-intrusively. However, changes of facial expressions, variations in pose, scale and illumination and the presence of hair and ornaments present some genuine challenges. In this paper, a 3D local feature based approach is proposed to fuse ear and face biometrics at the score level. Experiments with FRGC v.2 and the University of Notre Dame Biometric databases show that the technique achieves an identification rate of 98.71% and a verification rate of 99.68% (at 0.001 FAR) for fusion of the ear with neutral face biometrics. It is also found to be fast and robust to facial expressions achieving 98.1% and 96.83% identification and verification rates respectively.