Face Detection, Alignment, Quality Assessment and Attribute Analysis with Multi-Task Hybrid Convolutional Neural Networks

Release Date:2019-11-01  Author:ZTE  Click:

Face Detection, Alignment, Quality Assessment and Attribute Analysis with Multi-Task Hybrid Convolutional Neural Networks

GUO Da1,2 , ZHENG Qingfang3,4, PENG Xiaojiang1,2, and LIU Ming3,4
(1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. ZTE Corporation, Shenzhen, Guangdong 518057, China;
4. State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen, Guangdong 518057, China)

Abstract:This paper proposes a universal framework, termed as Multi-Task Hybrid Convolutional Neural Network (MHCNN), for joint face detection, facial landmark detection, facial quality, and facial attribute analysis. MHCNN consists of a high-accuracy single stage detector (SSD) and an efficient tiny convolutional neural network (T-CNN) for joint face detection refinement, alignment and attribute analysis. Though the SSD face detectors achieve promising results, we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes. By multi-task training, our T-CNN aims to provide five facial landmarks, facial quality scores, and facial attributes like wearing sunglasses and wearing masks. Since there is no public facial quality data and facial attribute data as we need, we contribute two datasets, namely FaceQ and FaceA, which are collected from the internet. Experiments show that our MHCNN achieves face detection performance comparable to the state of the art on FDDB, and gets reasonable results on AFLW, FaceQ and FaceA.
Keywords:face detection; face alignment; facial attribute; CNN; multi-task training

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