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Optical Engineering

Person-independent facial expression analysis by fusing multiscale cell features
Author(s): Lubing Zhou; Han Wang
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Paper Abstract

Automatic facial expression recognition is an interesting and challenging task. To achieve satisfactory accuracy, deriving a robust facial representation is especially important. A novel appearance-based feature, the multiscale cell local intensity increasing patterns (MC-LIIP), to represent facial images and conduct person-independent facial expression analysis is presented. The LIIP uses a decimal number to encode the texture or intensity distribution around each pixel via pixel-to-pixel intensity comparison. To boost noise resistance, MC-LIIP carries out comparison computation on the average values of scalable cells instead of individual pixels. The facial descriptor fuses region-based histograms of MC-LIIP features from various scales, so as to encode not only textural microstructures but also the macrostructures of facial images. Finally, a support vector machine classifier is applied for expression recognition. Experimental results on the CK+ and Karolinska directed emotional faces databases show the superiority of the proposed method.

Paper Details

Date Published: 4 March 2013
PDF: 9 pages
Opt. Eng. 52(3) 037201 doi: 10.1117/1.OE.52.3.037201
Published in: Optical Engineering Volume 52, Issue 3
Show Author Affiliations
Lubing Zhou, Nanyang Technological Univ. (Singapore)
Han Wang, Nanyang Technological Univ. (Singapore)

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