基于人脸深层特征和手势能量图像的多生物特征识别系统

Onur Can Kurban, T. Yıldırım, Ahmet Bilgic
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引用次数: 23

摘要

如今,随着生物识别数据的使用越来越多,人们期望系统能够稳定地工作,并且能够成功地应对困难的情况和伪造。在人脸识别系统中,诸如光线方向、面部表情和反射等变量使识别变得困难。通过生物特征融合,可以实现安全和高性能的结果。在这项工作中,使用Eurocom Kinect面部数据集和BodyLogin手势轮廓数据集创建虚拟数据集,并将它们与得分水平融合。人脸数据库采用VGG face深度学习模型作为特征提取器,能量成像方法提取手势特征。然后,通过主成分分析和相似度分数进行特征约简,得到标准差欧氏距离。结果表明,在不同的光照和表情条件下,深度学习特征的人脸识别效果都很好,而多生物特征的人脸识别结果具有更高的真实匹配率(GMR)和更低的错误接受率(FAR)。由于这一过程,手势能量成像可以用于人识别和多生物特征数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multi-biometric recognition system based on deep features of face and gesture energy image
Nowadays, with the increasing use of biometric data, it is expected that systems work robustly and they can give successful results against difficult situations and forgery. In face recognition systems, variables such as direction of light, facial expression and reflection makes identification difficult. With biometric fusion, both safe and high performance results can be achieved. In this work, Eurocom Kinect Face dataset and BodyLogin Gesture Silhouettes dataset are used to create a virtual dataset and they were fused with score level. For face database, VGG Face deep learning model was used as feature extractor and energy imaging method was used for extracting gesture features. Afterwards the features reduced by principal component analysis and similarity scores were produced with standard deviation Euclidean distance. The results show that face recognition achieved a high performance with deep learning features under different light and expression conditions, however, multi-biometric results have reached higher genuine match rate (GMR) performance and lower false acceptance rate (FAR). As a result of this process, gesture energy imaging can be used for person recognition and for multi biometric data.
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