{"title":"基于改进MTCNN的非均匀弱光下人脸检测","authors":"Yufei Bao, Rong Dang","doi":"10.1109/ICAICE54393.2021.00138","DOIUrl":null,"url":null,"abstract":"In order to improve the accuracy of face detection under non-uniform low-light conditions, an image preprocessing method based on illumination compensation and adaptive weight settings are proposed to apply to MTCNN's face detection algorithm. The algorithm uses light compensation theory to preprocess the face image, and then adds an adaptive module to the MTCNN model, which can significantly improve the accuracy and detection rate of MTCNN's face detection under non-uniform low-light conditions, making the accuracy rate reach 93.37 %. Experiments show that under non-uniform low light conditions, this method has better accuracy and robustness than the original MTCNN method, which is beneficial to the later face recognition task.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Face Detection under Non-uniform Low Light Based on Improved MTCNN\",\"authors\":\"Yufei Bao, Rong Dang\",\"doi\":\"10.1109/ICAICE54393.2021.00138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the accuracy of face detection under non-uniform low-light conditions, an image preprocessing method based on illumination compensation and adaptive weight settings are proposed to apply to MTCNN's face detection algorithm. The algorithm uses light compensation theory to preprocess the face image, and then adds an adaptive module to the MTCNN model, which can significantly improve the accuracy and detection rate of MTCNN's face detection under non-uniform low-light conditions, making the accuracy rate reach 93.37 %. Experiments show that under non-uniform low light conditions, this method has better accuracy and robustness than the original MTCNN method, which is beneficial to the later face recognition task.\",\"PeriodicalId\":388444,\"journal\":{\"name\":\"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAICE54393.2021.00138\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAICE54393.2021.00138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Face Detection under Non-uniform Low Light Based on Improved MTCNN
In order to improve the accuracy of face detection under non-uniform low-light conditions, an image preprocessing method based on illumination compensation and adaptive weight settings are proposed to apply to MTCNN's face detection algorithm. The algorithm uses light compensation theory to preprocess the face image, and then adds an adaptive module to the MTCNN model, which can significantly improve the accuracy and detection rate of MTCNN's face detection under non-uniform low-light conditions, making the accuracy rate reach 93.37 %. Experiments show that under non-uniform low light conditions, this method has better accuracy and robustness than the original MTCNN method, which is beneficial to the later face recognition task.