Computer and Information Science; Vol. 7, No. 3; 2014 ISSN 1913-8989 E-ISSN 1913-8997 Published by Canadian Center of Science and Education 31 Spontaneous Facial Expression Recognition Based on Histogram of Oriented Gradients Descriptor Manar M. F. Donia 1 , Aliaa A. A. Youssif 2 & Atallah Hashad 3 1 Modern Academy for Engineering & Technology, Computer Engineering Department, Cairo, Egypt 2 Faculty of Computers & Information, Helwan University, Helwan, Egypt 3 Arab Academy for Science, Technology & Maritime Transport, College of Engineering, Computer Engineering Department, Cairo, Egypt Correspondence: Manar M. F. Donia, Modern Academy for Engineering & Technology, Computer Engineering Department, Cairo, Egypt. E-mail: [email protected]Received: April 9, 2014 Accepted: April 23, 2014 Online Published: July 5, 2014 doi:10.5539/cis.v7n3p31 URL: http://dx.doi.org/10.5539/cis.v7n3p31 Abstract Automatically detecting facial expressions has become an important research area. It plays a significant role in security, human-computer interaction and health-care. Yet, earlier work focuses on posed facial expression. In this paper, we propose a spontaneous facial expression recognition method based on effective feature extraction and facial expression recognition for Micro Expression analysis. In feature extraction we used histogram of oriented gradients (HOG) descriptor to extract facial expression features. Expression recognition is performed by using a Support vector machine (SVM) classifier to recognize six emotions (happiness, anger, disgust, fear, sadness and surprise). Experiments show promising results of the proposed method with recognition accuracy of 95% on static images while 80% on videos. Keywords: Micro-Facial-Expression, emotion analysis, facial action coding system; human computer interaction, histogram of oriented gradients, face detection and tracking 1. Introduction Decision making is a part of our day to-day life. Facial expression help humans perceive useful information; make decisions and give instant responses during social communication. It is easy to fake, and this leads to incorrect decisions. Unlike facial expressions spontaneous micro expressions are difficult to hide or fake because they are involuntary expressions shown on human faces according to emotions experienced. They are very brief, lasting only 1/25 to 1/5 of a second. Currently only highly trained individuals are able to distinguish them, but even with proper training the recognition accuracy is only 47%. Combining computer vision and behavioral sciences have large potential for developing a technology, which helps in that aspect. The major challenges are: the short duration of expressions needs a camera with high frame rates; detection of light changes in facial skin needs effective feature data extraction, representation and facial expression recognition algorithm. Facial expression analysis goes well back into the nineteenth century. Darwin published his work on expression of emotions in man and animals. He claimed that we cannot understand human emotional expression without understanding the emotional expression in animals. In 1971 Ekman and Friesen postulated six primary emotions that each has a distinctive content together with a unique facial expression. Based on the work published (Porter & Brinke, 2008; Ekman, 2009) micro expressions are the most important behavioral source of lying detection and it used for danger demeanor detection as will. In this paper we propose a spontaneous facial expression analysis in which the face divided to specific regions, histogram oriented gradients calculated for each region to extract feature vector and apply the support vector machine to do classification. The system achieves very promising results that compare favorably with human accuracy and other methods of detection because we depend on effective feature extraction (HOG) which captures edge or gradient structure that is very characteristic of local shape, and it does so in a local representation with an easily controllable degree of invariance to local geometric and photometric transformations.
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Computer and Information Science; Vol. 7, No. 3; 2014
ISSN 1913-8989 E-ISSN 1913-8997
Published by Canadian Center of Science and Education
31
Spontaneous Facial Expression Recognition Based on Histogram of
Oriented Gradients Descriptor
Manar M. F. Donia1, Aliaa A. A. Youssif
2 & Atallah Hashad
3
1 Modern Academy for Engineering & Technology, Computer Engineering Department, Cairo, Egypt
2 Faculty of Computers & Information, Helwan University, Helwan, Egypt
3 Arab Academy for Science, Technology & Maritime Transport, College of Engineering, Computer Engineering
Department, Cairo, Egypt
Correspondence: Manar M. F. Donia, Modern Academy for Engineering & Technology, Computer Engineering