Improving Face Recognition by Pose-Aware Quality Assessment and Judgement

Face recognition has grown rapidly in the past several years due to advances in deep learning. More and more applications have emerged as this technology becomes more mature. However, face recognition under uncontrolled conditions is still quite challenging. For example, real-world applications usually encounter the issue of non-frontal standing pose which causes the face recognition system to degrade or even fail. Thus, this research work studies the issue of non-ideal facial pose in face recognition and propose to addresses this problem via pose-aware quality assessment and judgement. We first implement a standard face recognition system, consisting of an MTCNN face detection stage and a FaceNet face recognition stage. Then, we introduce a Quality Assessment and Judgement (QAJ) stage between the face detection stage and the face recognition stage. The QAJ stage conducts facial pose estimation which is realized through a DNN. Given a facial input, the QAJ stage assesses the facial pose and judges if the input is satisfactory in terms of quality. Inputs of poor quality will be screened and dropped out while inputs of high quality will be passed to the subsequent face recognition stage to output a final recognized identity. In the experiments, we compare the face recognition rates with and without the QAJ stage. Using a pose threshold of 15 degrees, we find out that the recognition rate is improved by 2.83%, which is a significant improvement on the recognition performance and justifies our proposed technique of QAJ.