A Real-time Face Detector on CPU Using Efficient Transformer

Abstract

Face detection is a basic vision method to find the fa-ciallocation. It is usually used in the initial step of advanced facial analysis. Therefore, this approach is required to work quickly, especially on low-cost devices to support practical applications. A deep learning architecture can robustly extract the distinctive feature by employing a lot of weighted filters. However, the model produces heavy parameters and computational complexity. A transformer is a deep learning architecture that can capture the feature position relationship, which increases the detector performance. This work in this paper proposes a new efficient transformer architecture that is implemented to face detection. It can highlight the spatial information from a similarity map by utilizing a 2D-convolutional filter. This architecture generates low computation and lightweight trainable parameters that serve the proposed face detector to run fast on an inexpensive device. As a result, this proposed network achieves high performance and competitive precision with the low-cost model. Additionally, the proposed transformer module does not significantly add computation and parameters that can run fast at 95 frames per second on a Core is CPU.