基于LBP特征的微笑真实性识别

K. Nurzynska, B. Smolka
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引用次数: 1

摘要

对人们来说,正确识别面部表情所表现出的情绪真实性是很困难的。然而,有一种观点认为,计算机系统能够感知到一些与准确性表达相关的微小变化,而这些变化对人类来说是不可见的,因此能够提高对情绪的正确感知。这项工作解决了自发和摆姿势的微笑识别问题,并提出了两种方法。第一种方法使用视觉线索,其中特征向量通过应用统一的局部二值模式来描述电影中均匀采样帧的内容。第二种方法是用从每帧提取的信息中提取的微笑强度信息来描述视频序列,其中的特征向量是利用这些数据计算的简单统计度量来构建的。这两种系统及其组合在UVA-NEMO数据库上进行了测试,并证明了令人鼓舞的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smile Veracity Recognition Using LBP Features for Image Sequence Processing
Correct recognition of emotion veracity exhibited in facial gestures is troublesome for people. Yet, there is a belief that computer systems are able to perceive some tiny changes correlated to veracity expression, invisible for people, and therefore are able to improve proper perception of emotions. This work addresses the problem of spontaneous and posed smile recognition and suggests two approaches. The first one uses the visual cues, in which the feature vector describes the content of evenly sampled frames in the movie by applying uniform local binary patterns. The second one, describes the video sequence with smile intensity information derived from information extracted from each frame, where the feature vector is built using simple statistical measures calculated from this data. These two systems and a combination of them are tested on UVA-NEMO database and proved to deliver encouraging results.
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