文章基本信息

标题：Facial Expression Detection for video sequences using local feature extraction algorithms
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作者：Kennedy Chengeta ; Serestina Viriri
期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2019
卷号：10
期号：1
页码：1-15
DOI：10.5121/sipij.2019.10103
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Facial expression image analysis can either be in the form of static image analysis or dynamic temporal 3D image or video analysis. The former involves static images taken on an individual at a specific point in time and is in 2-dimensional format. The latter involves dynamic textures extraction of video sequences extended in a temporal domain. Dynamic texture analysis involves short term facial expression movements in 3D in a temporal or spatial domain. Two feature extraction algorithms are used in 3D facial expression analysis namely holistic and local algorithms. Holistic algorithms analyze the whole face whilst the local algorithms analyze a facial image in small components namely nose, mouth, cheek and forehead. The paper uses a popular local feature extraction algorithm called LBP-TOP, dynamic image features based on video sequences in a temporal domain. Volume Local Binary Patterns combine texture, motion and appearance. VLBP and LBP-TOP outperformed other approaches by including local facial feature extraction algorithms which are resistant to gray-scale modifications and computation. It is also crucial to note that these emotions being natural reactions, recognition of feature selection and edge detection from the video sequences can increase accuracy and reduce the error rate. This can be achieved by removing unimportant information from the facial images. The results showed better percentage recognition rate by using local facial extraction algorithms like local binary patterns and local directional patterns than holistic algorithms like GLCM and Linear Discriminant Analysis. The study proposes local binary pattern variant LBP-TOP, local directional patterns and support vector machines aided by genetic algorithms for feature selection. The study was based on Facial Expressions and Emotions (FEED) and CK+ image sources..