Judgmental Feature Based Facial Expression Recognition and FER Datasets - A Comprehensive Study

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Published Mar 1, 2017
https://doi.org/10.47164/ijngc.v8i1.123
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Volume 8, Issue 1, March 2017

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Mahesh M Goyani
Research Scholar, Department of Computer Engineering, CSPIT, Charotar University of Science & Technology, Changa, Gujarat
Narendra M Patel
Associate Professor, Department of Computer Engineering, Birla Vishvakarma Mahavidyalaya, Vallabh Vidyanagar, Gujarat

Abstract

Facial expressions play an equally important role as the verbal communication and tonal expressions. They echo the mental state of the person. Expressions can be modeled either by using descriptive features - coded using facial muscles, or judgmental features - coded using texture information. In this paper, we surveyed mainly judgmental feature based prominent methods. An image is rich and high dimensional data structure, which can result into considerable computation when processed directly. Various feature extraction techniques have been proposed to represent the image efficiently in lower dimensions which can be easily processed by a machine. Until now, most of the research was centered around the recognition of frontal face expressions. Recent work has been targeted on processing profile faces and spontaneous expressions by treatment of multimodal fusion. In addition to features, the dataset is another major aspect of pattern recognition. Multidimensional comparison of various facial expression databases is also derived in this paper. Moreover, the survey presents scientific challenges touching the performance of the system.

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How to Cite
Goyani, M. M. ., & Patel, N. M. . (2017). Judgmental Feature Based Facial Expression Recognition and FER Datasets - A Comprehensive Study. International Journal of Next-Generation Computing, 8(1), 62–81. https://doi.org/10.47164/ijngc.v8i1.123
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