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Feature-Based Facial Expression Recognition: Experiments With a Multi-Layer Perceptron

Zhengyou Zhang 1
1 ROBOTVIS - Computer Vision and Robotics
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In this paper, we report our experiments on feature-based facial expression recognition within an architecture based on a two-layer perceptron. We investigate the use of two types of features extracted from face images: the geometric positions of a set of fiducialpoints on a face, and a set of multi-scale and multi-orientation Gabor wavelet coefficients at these points. They can be used either independently or jointly. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furthermore, since the first layer of the perceptron actually performs a nonlinear reduction of the dimensionality of the feature space, we have also studied the desired number of hidden units, i.e., the appropriate dimension to represent a facial expression in order to achieve a good recognition rate. It turns out that five to seven hidden units are probably enough to represent the space of feature expressions. Then, we have investigat- ed the importance ofeach individual fiducial point to facial expression recognition. Sensitivity analysis reveals that points on cheeks and on forehead carry little useful information. After discarding them, not only the computational efficiency increases, but also the generalization performanc- e slightly improves. Finally, we have studied the significance of image scales. Experiments show that facial expression recognition is mainly a low frequency process, and a spatial resolution of 64 pixels $\times$ 64 pixels is probably enough.
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Submitted on : Wednesday, May 24, 2006 - 12:34:22 PM
Last modification on : Saturday, January 27, 2018 - 1:30:56 AM
Long-term archiving on: : Sunday, April 4, 2010 - 11:42:55 PM


  • HAL Id : inria-00073335, version 1



Zhengyou Zhang. Feature-Based Facial Expression Recognition: Experiments With a Multi-Layer Perceptron. RR-3354, INRIA. 1998. ⟨inria-00073335⟩



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