Face in video recognition (FiVR) is widely used in video surveillance and video analytic. Various solutions have been proposed to improve the performance of face detection, frame selection and face recognition in FiVR systems. However, all these methods have a common inherent ceiling", which is defined by the source video's quality. One key factor causing face image quality loss is video compression standards. To address this challenge, in this paper, first, we analysis and quantify the effects of video compression on the FiVR performance; secondly, we propose to use deep learning based model to mitigate artifacts in compressed input video. We apply the image based convolutional auto-encoder (CAE) to extract the features of input face images and restore them towards less artifacts. From the experimental results, our approach can mitigate artifacts on face images extracted from compressed videos and improve the overall face recognition (FR) performance by as much as 50% in TPR (True Positive Rate) at the same FPR (False Positive Rate) value.
{"title":"Mitigate compression artifacts for face in video recognition","authors":"Xuan Qi, Chen Liu","doi":"10.1117/12.2556702","DOIUrl":"https://doi.org/10.1117/12.2556702","url":null,"abstract":"Face in video recognition (FiVR) is widely used in video surveillance and video analytic. Various solutions have been proposed to improve the performance of face detection, frame selection and face recognition in FiVR systems. However, all these methods have a common inherent ceiling\", which is defined by the source video's quality. One key factor causing face image quality loss is video compression standards. To address this challenge, in this paper, first, we analysis and quantify the effects of video compression on the FiVR performance; secondly, we propose to use deep learning based model to mitigate artifacts in compressed input video. We apply the image based convolutional auto-encoder (CAE) to extract the features of input face images and restore them towards less artifacts. From the experimental results, our approach can mitigate artifacts on face images extracted from compressed videos and improve the overall face recognition (FR) performance by as much as 50% in TPR (True Positive Rate) at the same FPR (False Positive Rate) value.","PeriodicalId":105783,"journal":{"name":"Disruptive Technologies in Information Sciences IV","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114638495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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