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Emotion Recognition Using Facial Expression

Emotion Recognition Using Facial Expression

Santosh Kumar, Shubam Jaiswal, Rahul Kumar, Sanjay Kumar Singh
Copyright: © 2018 |Pages: 20
ISBN13: 9781522552048|ISBN10: 1522552049|EISBN13: 9781522552055
DOI: 10.4018/978-1-5225-5204-8.ch074
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MLA

Kumar, Santosh, et al. "Emotion Recognition Using Facial Expression." Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 1768-1787. https://doi.org/10.4018/978-1-5225-5204-8.ch074

APA

Kumar, S., Jaiswal, S., Kumar, R., & Singh, S. K. (2018). Emotion Recognition Using Facial Expression. In I. Management Association (Ed.), Computer Vision: Concepts, Methodologies, Tools, and Applications (pp. 1768-1787). IGI Global. https://doi.org/10.4018/978-1-5225-5204-8.ch074

Chicago

Kumar, Santosh, et al. "Emotion Recognition Using Facial Expression." In Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1768-1787. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5204-8.ch074

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Abstract

Recognition of facial expression is a challenging problem for machine in comparison to human and it has encouraged numerous advanced machine learning algorithms. It is one of the methods for emotion recognition as the emotion of a particular person can be found out by studying his or her facial expressions. In this paper, we proposes a generic algorithms for recognition of emotions and illustrates a fundamental steps of the four algorithms such as Eigenfaces (Principal Component Analysis [PCA]), Fisherfaces, Local Binary Pattern Histogram (LBP) and SURF with FLANN over two databases Cohn-kanade database and IIT BHU student face images as benchmark database.The objective of this book chapter is to recognize the emotions from facial images of individuals and compare the performances of holistic algorithms like Eigenfaces, Fisherfaces, and texture based recognition algorithms LBPH, hybrid algorithm SURF and FLANN. Matching efficiency of individual emotions from facial expression databases are labeled for training and testing phases. The set of features is extracted from labeled dataset for training purpose and test images are matched with discriminative set of feature points. Based on that comparison, we conclude that Eigenfaces and Fisherfaces yields good recognition accuracy on the benchmark database than others and the efficiency of SURF with FLANN algorithm can be enhanced significantly by changing the parameters.

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