Efficient technique for real-time face detection

Abstract

Currently, the top-performing face detectors use Convolution Neural Networks (CNN). These detectors have high performance but require large computational resources. Hence, it is a challenge to use these for practical applications with limited resources. To address this problem, this research paper proposes a single-stage face detector for practical applications with better accuracy and speed. The proposed face detector is a lightweight and fast CNN-based face detector. To achieve fast detection speed, by swiftly reducing the size of the input feature space through a carefully designed architecture using depth-wise convolution and Receptive Field Blocks (RBF). The high accuracy of the method is achieved by preserving the feature information from the early layers using CReLU (Concatenate Rectified Linear Units) activation in the CNN architecture. To detect faces of different pixel sizes, we used parallel RF blocks, which use multiple filters of different kernel sizes and dilation. The proposed method was tested on benchmark datasets such as WIDER FACE, AFW, and PASCAL faces, and performed well when compared to recent state-of-the-art real-time face detection algorithms.