Can holistic representations be used for face biometric quality assessment?

A face quality metric must quantitatively measure the usability of an image as a biometric sample. Though it is well established that quality measures are an integral part of robust face recognition systems, automatic measurement of bio-metric quality in face is still challenging. Inspired by scene recognition research, this paper investigates the use of holistic super-ordinate representations, namely, Gist and sparsely pooled Histogram of Orientated Gradient (HOG), in classifying images into different quality categories that are derived from matching performance. The experiments on the CAS-PEAL and SCFace databases containing covariates such as illumination, expression, pose, low-resolution and occlusion by accessories, suggest that the proposed algorithm can efficiently classify input face image into relevant quality categories and be utilized in face recognition systems.