Research on Multi-Angle Face Detection Method Based on Improved YOLOV2 Algorithm

Abstract Face detection based on the YOLOV2 algorithm can achieve a higher level of accuracy and show a faster detection speed. Therefore, the YOLOV2 algorithm appears more frequently in the field of target real-time detection. The traditional YOLOV2 algorithm uses the Darknet-19 network. Based on t...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1848; no. 1; p. 12024
Main Authors Zhang, Huanping, Cao, Hanhua, Liu, Zhendan, Zhou, Yuhuai, Wang, Yujuan
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.04.2021
Subjects
Algorithms
Face recognition
Image enhancement
Physics
Target detection
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Summary:Abstract Face detection based on the YOLOV2 algorithm can achieve a higher level of accuracy and show a faster detection speed. Therefore, the YOLOV2 algorithm appears more frequently in the field of target real-time detection. The traditional YOLOV2 algorithm uses the Darknet-19 network. Based on this, this paper builds a new network by merging the residual network (ResNet). In addition, in order to further make up for the defects exposed by the YOLOV2 algorithm in face detection, we change the weight of the loss function and implement an image enhancement scheme on the training set. The YOLOV2 algorithm and the improved YOLOV2 algorithm are compared and tested on the Wider Face dataset, and then we use face data sets from different angles to test the performance of the algorithm. According to the analysis of the experimental results, the improved YOLOV2 algorithm can show higher accuracy and robustness in multi-angle face detection.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1848/1/012024