Evaluation of fusion approaches for face recognition using light field cameras

Although face recognition is a widely accepted form of biometrics due to its ease of capture, it still suffers from out-of-focus imaging due to the limited depth-of-field found in conventional 2D imaging systems. In order to overcome such a challenge, light field cameras have been successfully employed in biometrics. The number of depth images obtained from a light field camera can be optimally used by fusing a number of these images or selecting the best focus image from the set of images. In this work, we employ a method to pre-select the images when more than two images corresponding to different focus are present. Further, we evaluate various fusion techniques against the all - in - focus image obtained from light field imaging for the verification performance in face recognition systems. The evaluation of various schemes is performed on one of the largest light field face databases consisting of 80 subjects. The best Equal Error Rate (EER) of 4.14% is observed with the combination of employed pre-selection method and Laplacian Pyramid based fusion approach using both sparse representation and multi-scale transform.