{"title":"基于kinect深度数据的3D人脸识别","authors":"Zijun Cheng, Tianwei Shi, Wenhua Cui, Yunqi Dong, Xuehan Fang","doi":"10.1109/ICSAI.2017.8248353","DOIUrl":null,"url":null,"abstract":"In this paper, a contour map human facial recognition algorithm is proposed to implement the three-dimensional (3D) face recognition with the Kinect Xbox One. Since the scale of 3D depth data collected from Kinect is tremendous, the face recognition process cannot be handled in real time. To improve the speed and accuracy of the recognition process, the proposed algorithm turns the 3D depth data to the two-dimensional (2D) contour map. Furthermore, due to the 3D depth data obtained by Kinect, there is no need of expensive, ponderous and slow 3D scanners. Ten male and female subjects were involved in the validation experiment and the results verify that the proposed algorithm was feasible for face recognition. In addition, compared with other methods, Eigenface, Local Binary Patterns (LBP) and Linear Discriminant Analysis (LDA), the proposed algorithm has the better security and reliability.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"3D face recognition based on kinect depth data\",\"authors\":\"Zijun Cheng, Tianwei Shi, Wenhua Cui, Yunqi Dong, Xuehan Fang\",\"doi\":\"10.1109/ICSAI.2017.8248353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a contour map human facial recognition algorithm is proposed to implement the three-dimensional (3D) face recognition with the Kinect Xbox One. Since the scale of 3D depth data collected from Kinect is tremendous, the face recognition process cannot be handled in real time. To improve the speed and accuracy of the recognition process, the proposed algorithm turns the 3D depth data to the two-dimensional (2D) contour map. Furthermore, due to the 3D depth data obtained by Kinect, there is no need of expensive, ponderous and slow 3D scanners. Ten male and female subjects were involved in the validation experiment and the results verify that the proposed algorithm was feasible for face recognition. In addition, compared with other methods, Eigenface, Local Binary Patterns (LBP) and Linear Discriminant Analysis (LDA), the proposed algorithm has the better security and reliability.\",\"PeriodicalId\":285726,\"journal\":{\"name\":\"2017 4th International Conference on Systems and Informatics (ICSAI)\",\"volume\":\"76 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 4th International Conference on Systems and Informatics (ICSAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSAI.2017.8248353\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 4th International Conference on Systems and Informatics (ICSAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAI.2017.8248353","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
摘要
针对Kinect Xbox One的三维人脸识别问题,提出了一种等高线地图人脸识别算法。由于Kinect采集的3D深度数据规模巨大,人脸识别过程无法实时处理。为了提高识别过程的速度和准确性,该算法将三维深度数据转换为二维等高线地图。此外,由于Kinect获得的3D深度数据,不需要昂贵、笨重和缓慢的3D扫描仪。通过10名男性和10名女性受试者进行验证实验,结果验证了所提出的算法在人脸识别方面的可行性。此外,与特征脸(Eigenface)、局部二值模式(Local Binary Patterns, LBP)和线性判别分析(Linear Discriminant Analysis, LDA)等方法相比,该算法具有更好的安全性和可靠性。
In this paper, a contour map human facial recognition algorithm is proposed to implement the three-dimensional (3D) face recognition with the Kinect Xbox One. Since the scale of 3D depth data collected from Kinect is tremendous, the face recognition process cannot be handled in real time. To improve the speed and accuracy of the recognition process, the proposed algorithm turns the 3D depth data to the two-dimensional (2D) contour map. Furthermore, due to the 3D depth data obtained by Kinect, there is no need of expensive, ponderous and slow 3D scanners. Ten male and female subjects were involved in the validation experiment and the results verify that the proposed algorithm was feasible for face recognition. In addition, compared with other methods, Eigenface, Local Binary Patterns (LBP) and Linear Discriminant Analysis (LDA), the proposed algorithm has the better security and reliability.