ZTE Communications ›› 2019, Vol. 17 ›› Issue (3): 15-22.doi: 10.12142/ZTECOM.201903004
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GUO Da1,2, ZHENG Qingfang3,4, PENG Xiaojiang1,2, LIU Ming3,4
Received:2019-06-11
Online:2019-09-29
Published:2019-12-06
About author:GUO Da received the B.Eng. from the Computer Engineering College, JiMei University, China in 2018. He is currently a master student at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. His research direction is face detection and recognition based on deep learning.|ZHENG Qingfang received the B.S. degree in civil engineering from Shanghai Jiaotong University, China in 2002 and Ph.D. degree in computer science from Institute of Computing Technology, Chinese Academy of Science, China in 2008. He is currently the chief scientist of video technology with ZTE Corporation. His research interests include computer vision, multimedia retrieval, image/video processing, with a special focus on low power embedded application and large-scale cloud application.|PENG Xiaojiang (xj.peng@siat.ac.cn) received his Ph.D. from School of Information Science and Technology from Southwest Jiaotong University, China in 2014. He currently is an associate professor at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. He was a postdoctoral researcher at Idiap Institute, Switzerland from 2016 to 2017, and was a postdoctoral researcher in LEAR Team, INRIA, France, working with Prof. Cordelia Schmid from 2015 to 2016. He serves as a reviewer for IJCV, TMM, TIP, CVPR, ICCV, AAAI, IJCAI, FG, Image and Vision Computing, IEEE Signal Processing Letter, Neurocomputing, etc. His research focus is in the areas of action recognition and detection, face recognition, facial emotion analysis, and deep learning.|LIU Ming received the M.Sc. degree from Harbin Engineering University, China in 2011. He is currently a senior engineer with ZTE Corporation. His research interests include object detection, tracking and recognition.
GUO Da, ZHENG Qingfang, PENG Xiaojiang, LIU Ming. Face Detection, Alignment, Quality Assessment and Attribute Analysis with Multi-Task Hybrid Convolutional Neural Networks[J]. ZTE Communications, 2019, 17(3): 15-22.
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Figure 1.
The pipeline of the proposed Multi-Task Hybrid Convolutional Neural Network (MHCNN). It consists of an SSD-based face detector for high-accuracy detection performance and a T-CNN for detection refinement and multi-task face analysis."
Figure 2.
The architecture of single stage detector (SSD)-based face detector."
Figure 3.
The architecture of tiny CNN (T-CNN)."
Figure 4.
Examples of our FaceA dataset."
Figure 5.
Examples of our FaceQ dataset."
Figure 6.
Discontinuous ROC curves on the FDDB dataset."
Table 1
Comparison of our MHCNN on FDDB"
| Methods | Recall |
|---|---|
| Cascade CNN [ | 85.67% |
| ACF-multiscale [ | 86.08% |
| YAN et al. [ | 86.15% |
| Faster R-CNN [ | 96.10% |
| S3FD [ | 98.37% |
| MHCNN | 98.66% |
Table 2
Ablation study of T-CNN on the FaceA dataset"
| Methods (task setting) | Accuracy of Sunglasses (Threshold = 0.5) | Accuracy of Mask (Threshold = 0.5) |
|---|---|---|
| T-CNN (sunglasses) | 76.14% | ---- |
| T-CNN (sunglasses + landmarks) | 76.57% | ---- |
| T-CNN (masks) | ---- | 83.30% |
| T-CNN (masks + landmarks) | ---- | 85.90% |
| T-CNN (sunglasses + masks + landmarks) | 98.70% | 99.35% |
Figure 7.
False positives on FaceA test set. The score of images are the probability to predict wearing-mask and wearing-sunglasses."
Table 3
Evaluation on the FaceQ dataset"
| Methods | Accuracy of Face Quality (Best threshold) |
|---|---|
| LBP+SVM | 78.52% |
| T-CNN | 81.86% |
Figure 8.
False positives on FaceQ test set. The faces with higher scores are predicted to the high-quality class and those with lower scores are predicted to the low-quality class."
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