Abstract:

To address the difficulty of balancing the speed and accuracy of face capture systems embedded in mobile devices, a fast and accurate embedded face capture system is designed based on the combination of a lightweight neural network and a Hash tracking algorithm. Firstly, an optimised face detection network is constructed based on the lightweight MobileNet solid state disk (SSD) object detector. After pruning and face alignment, a Hash algorithm is combined to track a face based on the average Hash (aHash) and a perceptual Hash (pHash) design, in which the key point distance and face size as well as the four-direction Sobel operator are used as the three standards to extract the best face image. Finally, MobileFaceNet is applied to identify the best face. The experimental results indicate that the speed of the proposed face detection algorithm is increased by 22.6% compared to using the standard MobileNet SSD. Furthermore, compared with using only the mean Hash or the pHash algorithm, the matching accuracy is improved by 21.7% and 10.1% when the combined Hash algorithm is applied, respectively. The face capture accuracy is greater than 95%, and the capture speed can reach 28 frame/s.

Key words: face capture, embedded, Hash tracking, deep learning