Multi-face detection and alignment using multiple kernels

Abstract

Multi-face detection and alignment techniques under unlimited environment are challenging issues. In recent years, the demand for face detection and alignment techniques has increased in many areas, including automatic drive and security. However, some mainstream algorithms, such as the Multi-task Cascaded Convolutional Networks (MTCNN) algorithm cannot transfer to multi-face detection and alignment, which introduces new multi-size, multi-resolution, and multi-angle challenges. This paper proposes an improved algorithm—Multi-face-MTCNN for precise original face detection and alignment algorithm when there is an overlapping face scenario. We design two new network structures: Pixelfusion-MTCNN and Twoconv-MTCNN. Moreover, we propose new data augmentation method and optimized detection process which are applied in the Multi-face-MTCNN algorithm. The limitations associated with single-scale kernel size in MTCNN are solved to obtain a satisfactory performance. Compared to the MTCNN algorithm, experimental analysis of the FDDB dataset show that 1.766% improves Multi-face-MTCNN. Meanwhile, on the WIDER FACE verification benchmark, respectively, with regards to the three sub-datasets, the proposed algorithm’s performance is improved by 3.426%, 2.776%, and 21.576%. Besides, average alignment error of the left eye, right eye, nose, left mouth, and right mouth is performed on the proposed algorithm.