Person identification using automatic integration of speech, lip, and face experts

N. Fox, R. Gross, P. Chazal, J. Cohn, R. Reilly
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引用次数: 46

Abstract

This paper presents a multi-expert person identification system based on the integration of three separate systems employing audio features, static face images and lip motion features respectively. Audio person identification was carried out using a text dependent Hidden Markov Model methodology. Modeling of the lip motion was carried out using Gaussian probability density functions. The static image based identification was carried out using the FaceIt system. Experiments were conducted with 251 subjects from the XM2VTS audio-visual database. Late integration using automatic weights was employed to combine the three experts. The integration strategy adapts automatically to the audio noise conditions. It was found that the integration of the three experts improved the person identification accuracies for both clean and noisy audio conditions compared with the audio only case. For audio, FaceIt, lip motion, and tri-expert identification, maximum accuracies achieved were 98%, 93.22%, 86.37% and 100% respectively. Maximum bi-expert integration of the two visual experts achieved an identification accuracy of 96.8% which is comparable to the best audio accuracy of 98%.
使用语音,嘴唇和面部专家自动集成的人识别
本文提出了一种基于音频特征、静态人脸图像和唇动特征三个独立系统集成的多专家人物识别系统。音频识别使用文本依赖的隐马尔可夫模型方法进行。采用高斯概率密度函数对唇部运动进行建模。利用FaceIt系统进行了基于静态图像的识别。实验对象为251名来自XM2VTS视听数据库的被试。采用自动权值的后期集成方法对三个专家进行组合。该集成策略可自动适应音频噪声条件。结果表明,与仅使用音频的情况相比,三位专家的结合提高了在干净音频和嘈杂音频条件下的人物识别准确率。对于音频、FaceIt、唇动和三专家识别,最大准确率分别为98%、93.22%、86.37%和100%。两位视觉专家的最大双专家集成实现了96.8%的识别准确率,与98%的最佳音频准确率相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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