Lightweight Convolutional Neural Network for Real-Time Face Detector on CPU Supporting Interaction of Service Robot

Abstract

Face detection plays an essential role in the success of the interaction between service robots and consumers. This method is the initial stage for face-related applications. Practical applications require face detection to work in real-time and can be implemented on low-cost devices such as CPU. Traditional methods have problems when the face is not frontal, blocked, and partially covered, but real-time speed is not an obstacle. On the other hand, deep learning has succeeded in accurately distinguishing facial features and backgrounds. Face sizes that tend to be medium and large when robot interaction with consumers so it can employ Convolutional Neural Networks (CNN) with light weights. In this paper, a real-time face detector is built that can work on the CPU. This detector will be implemented explicitly in service robots to support interactions with consumers. It can overcome the occlusion and not-frontal face. Detector architecture consists of the backbone as rapidly features extractor, transition module as a transformer of prediction map, and the dual-detection layer is head of a network prediction based on scale assignment. As a result, the detector can work at speeds of 301 frames per second on CPU without ignoring the accuracy.