[1]XU Hao,QIAN Yuhua,WANG Keqi,et al.Low-light face detection method based on the cross fusion of high-and low-frequency channel features[J].CAAI Transactions on Intelligent Systems,2024,19(2):472-481.[doi:10.11992/tis.202208034]
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Low-light face detection method based on the cross fusion of high-and low-frequency channel features

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 472-481 Column: 人工智能院长论坛 Public date: 2024-03-05
Title:
Low-light face detection method based on the cross fusion of high-and low-frequency channel features
Author(s):
XU Hao1; 2;  QIAN Yuhua1; 2; 3;  WANG Keqi1;  LIU Chang1; 2;  LI Junxia1; 2
1. Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, China;
2. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;
3. Key Laboratory of Computational Intelligence and Chinese Informat
Keywords:
low-light face detection; features of high-and low-frequency channels; low-light enhancement; multiscale feature fusion; computer vision; image processing; deep learning; frequency domain analysis
CLC:
TP183
DOI:
10.11992/tis.202208034
Abstract:
Face images captured under low-light conditions suffer from significant noise and low contrast, which negatively impact the accuracy of existing face detection systems. In addition, existing low-light image detection algorithms struggle to extract information from small facial areas. To tackle these issues, this paper proposes a two-stage face detection algorithm based on deep learning. This algorithm enhances low-light images before initiating the detection process using an established low-light image enhancement method. The objective is to enhance the ability of the detection method to extract facial information. Thus, a new cross-fusion method of high- and low-frequency channel features is designed. The first step involves using a separable module for high- and low-frequency channel features, enabling the separation of different scale features. These separated features are then cross-fused to improve the ability of the network to extract high-frequency details and low-frequency color information. This, in turn, improves the performance of the detection network. The comparative and ablation experiments validate the effectiveness of the proposed method. The experimental results demonstrate that our method surpasses the baseline method by 4.0% mAP.
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