4 March 2013 Person-independent facial expression analysis by fusing multiscale cell features
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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.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Lubing Zhou, Lubing Zhou, Han Wang, Han Wang, } "Person-independent facial expression analysis by fusing multiscale cell features," Optical Engineering 52(3), 037201 (4 March 2013). https://doi.org/10.1117/1.OE.52.3.037201 . Submission:
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