FHOG öznitelikleri ile yüz ifadesi tanima

Facial expression recognition is a popular computer vision subject that has many applications such as humancomputer interaction and behavior analysis. As for many computer vision problems, lighting and contrast differences increase the difficulty of the problem. Especially the non-planar structure of the face, protruding regions such as the nose and chin and recessed regions such as eye sockets cause variations in lighting. Another problem with facial expression recognition problems is that the multi-scale detection methods do not align the faces accurately. This leads to comparing features that are extracted from different facial regions, which degrades performance. FHOG features are a contrast-sensitive variation of histogram oriented gradients (HOG) features, which perform well at object detection applications. In this study, the performance of FHOG features at facial expression recognition is investigated. Additionally, aligning with respect to the facial landmarks is proposed to prevent performance degradation due to misalignment. The proposed method is shown to deliver 93% accuracy in facial expression recognition in the extended Cohn-Kanade dataset.