A New Facial Detection Model based on the Faster R-CNN

2018 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

材料科学;无线电电子学;计算机科学

Hao, Long^1 ; Jiang, F.^1

College of Electronic Engineering, Guangxi Normal University, Guilin

541004, China^1

关键词: CNN models;    Detection approach;    Detection models;    Iterative refinement;    Object detecting;    Region proposals;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/439/3/032117/pdf
DOI  :  10.1088/1757-899X/439/3/032117

来源: IOP

【 摘 要 】

The object detection approaches in conjunction with Fast/Faster R-CNN and YOLO have shown the benchmarking performance on several occasions. Inspired by the Refine Net, we propose a new model called Faster+ R-CNN based on Faster R-CNN, which is mainly based on iterative refinement on the proposed regions. The Faster+ R-CNN model can iteratively refine the region proposal based on previous output. We trained and tested our new model on PASCAL VOC 2007 dataset, and experiments showed that our method can iteratively improve the mean average precision (mAP) from 0.6702 to 0.6764 in object detecting task. We also demonstrate the facial detection results using the Faster+ R-CNN on the widely used Face Detection Dataset and Benchmark (FDDB) benchmark. By training the Faster+ R-CNN model on the large scale WIDER face dataset, we report the improved results on two widely used face detection benchmarks including FDDB.