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Impact of the face registration techniques on facial expressions recognition

机译:面部登记技术对面部表情识别的影响

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摘要

AbstractRecent methodologies for facial expression recognition have been proposed and have obtained good results in near-frontal view. However, these situations do not fairly represent in-the-wild challenges, where expressions are natural and the subject is free of its movement. This is reflected in the accuracy drop of facial expression methods obtained on recent databases. Two challenges (head pose variations and large displacements) in facial expression recognition are studied in this paper. Experiments are proposed in order to quantify the impact of free head movements using representative expression recognition approaches (LBP, LBP-TOP, HOOF). We propose an experimental protocol (SNaP-2DFe) that records, under controlled light, facial expressions with two cameras: one attached on the head and one placed in front of the subject. As in both cameras facial expressions are the same, differences in performances measured on each camera show the impact of head pose variations and large displacements on the underlying recognition approach.Highlights?Quantify the impact of free head movements on expression recognition performances.?Study the ability of face registration techniques to preserve the facial expression.?Synchronized frontal and non-frontal data for enhancing new registration techniques.]]>
机译:<![cdata [ Abstract 最近的面部表情识别方法的方法已经提出并获得了近额视视图的良好结果。然而,这些情况并不相当代表野外的挑战,其中表达是自然的,主题是没有运动的。这反映在最近数据库上获得的面部表情方法的精度下降。本文研究了面部表情识别中的两个挑战(头部姿势变化和大型位移)。提出了使用代表表达识别方法(LBP,LBP-TOP,HOOF)来量化自由头部运动的影响。我们提出了一种实验协议(SNAP-2DFE),记录,下方的带有两台相机的面部表情:一个连接在头部的一个,一个放置在主题前面。如在两个相机面部表达式中都是相同的,在每种相机上测量的性能的差异显示头部姿势变化和大型位移对底层识别方法的影响。 < / ce:abstract> 亮点 量化自由头部运动对表达识别性能的影响。 研究面部登记技术保留面部表情的能力。< / ce:para> 同步的正面和非正面数据,用于增强新的注册技术。 ]]>

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