Face biometric quality assessment via light CNN

In this paper, we proposed a novel biometric quality assessment (BQA) method for face images and explored its applications in face recognition. Here, we considered five categories of common homogeneous distortion in video suvillance applications, i.e. low-resolution, blurring, additive Gaussian white noise, salt and pepper noise, and Poisson noise. In the BQA model, we first learnt a classifier to simultaneously predict the categories and degrees of the degradation in a face image. Because the quality labels are often ambiguous and inaccurate, we used a light convolutional neural network with the Max-Feature-Map units to make the BQA model robust to noisy labels. Afterwards, we calculated the biometric quality score by pooling such predictions based on the recognition confidence of each degradation class. Finally, we proposed one promising strategy for developing reliable face recognition systems based on this BQA method. Thorough experiments have been conducted on the CASIA, FLW, and YouTube databases. The results demonstrate the effectiveness of the proposed BQA method.