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Performance Analysis of Facial Expression Recognition Schemes

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IJCA Proceedings on National Conference on Advance Trends in Information Technology
© 2013 by IJCA Journal
NCATIT - Number 1
Year of Publication: 2013
Authors:
E. Mary Shyla
M. Punithavalli

Mary E Shyla and M Punithavalli. Article: Performance Analysis of Facial Expression Recognition Schemes. IJCA Proceedings on National Conference on Advance Trends in Information Technology NCATIT:1-7, April 2013. Full text available. BibTeX

@article{key:article,
	author = {E. Mary Shyla and M. Punithavalli},
	title = {Article: Performance Analysis of Facial Expression Recognition Schemes},
	journal = {IJCA Proceedings on National Conference on Advance Trends in Information Technology},
	year = {2013},
	volume = {NCATIT},
	pages = {1-7},
	month = {April},
	note = {Full text available}
}

Abstract

Face recognition plays an important vision task having many practical applications such as biometrics, video surveillance, image retrieval, and human computer interaction. Most recently facial expression recognition has been focused for biometric facial recognition system in various confidential and high secured operational areas. Information for biometric representation and recognition are available in image space, scale and orientation. Combinatorial analysis of space, scale and orientation provide enriched features for more accurate biometric facial recognition. Position, spatial frequency and orientation selectivity properties of facial feature components play major role in visual perception. There are various methods have discussed for Facial Expression Recognition scheme for human biometric recognition. In this work we analyze current Facial Recognition schemes and provide an overview of the emerging Facial Expression Recognition methods and related research work done in this area. Also comparisons are done between the various schemes to clarify benefits and limitations. There are different measures on which performance of Facial Expression Recognition scheme depends, such as Recognition rate, Illumination variance, Expressional changes, Time differences, Facial color code, Size of feature components, False Positive and Negative and True Positive and Negative. Experimental Results shows that the performance analysis of the Facial Expression Recognition schemes on the basis of Recognition rate, Illumination variance, False Positive and True positive rate.

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