Multi-scale Face Detection Algorithm with Texture Feature Enhancement
Authors: 
Shiji He, Gangyang Nan, Xuewen Wang, Xue BaiAuthors Info & Claims
Shiji He, Gangyang Nan, Xuewen Wang, Xue BaiAuthors Info & ClaimsPages 993 - 999
Abstract
A multi-scale face detection algorithm with texture feature enhancement is proposed for the difficult problem of tiny and occluded face detection in natural scenes. In order to obtain more fine-grained face texture features and enhance the ability of tiny face detection, the backbone network structure of the baseline algorithm was adjusted. The feature enhancement module was introduced to obtain rich contextual feature information and improve local semantic information characterization, which facilitated detection of tiny and occluded faces by fusing the feature of different receptive fields. To increase the neck network's ability, an improved shuffle attention module ISAM was adopted to perceive face detail information and facilitate the performance of occlusion and tiny face detection. Multi-task Associative Detector Head (MTADH) is used to learn the correlation between the classification task and the regression task to better utilize regression features and classification features to improve face detection results. By controlling the width and depth of the detection networks, two detection networks with different sizes, MFDTFE-M and MFDTFE-S, are constructed. On the WIDER FACE validation dataset, the average precision of the two detection networks on the Hard subset is improved by 2.99% and 2.77% relative to the m-model and s-model of the baseline algorithm, and the performance of the tiny and occluded face detection is significantly improved.
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Index Terms
- Multi-scale Face Detection Algorithm with Texture Feature Enhancement
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Published In
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
Copyright © 2023 ACM.
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Published: 17 April 2024
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EITCE 2023
EITCE 2023: 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 20 - 22, 2023
Xiamen, China
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Overall Acceptance Rate 508 of 972 submissions, 52%
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