A Multi-Tasked Approach Towards Face Detection and Face Quality Assessment

Face in Video Recognition (FiVR) commonly follows a sequential pipeline of face detection, face quality assessment, and face recognition. However, performing these often machine learning-based tasks sequentially in real-time is a challenge when considering the excessive overhead caused by convolution and other feature extraction operations typically seen in neural networks employed across these stages. To overcome this challenge, a process that can perform these operations in parallel is needed. In this paper, we propose a methodology that can alleviate the constraints of real-time processing found in the sequential pipeline of FiVR. We exploit the similarities in features used in face detection and face quality assessment, hence designing a multi-tasked face detection and quality assessment network which can perform our FiVR operations with less inference time without sparing prediction accuracy. We evaluated the face quality prediction performance of our proposed approach in comparison with a stand-alone face quality network. We also evaluated the reduction in inference time by comparing the prediction speed of our multi-tasked face detection and quality network against its sequential counterparts. Our experimental results show that our multi-tasked model can successfully meet real-time processing demand while performing at the same level of accuracy as the sequential stand-alone models.