Emotion Recognition From Facial Expression In A Noisy Environment

Abstract

This study presents emotion recognition from facial expressions in a noisy environment. The challenges addressed in this study are noise in the images and illumination changes. Wavelets have been extensively used for noise reduction; therefore, we have applied wavelet and curvelet analysis from noisy images. The experiments are performed with different values of Gaussian noise (mean: 0.01, 0.03) and (variance: 0.01, 0.03). Similarly, for experimentation with illumination changes, we have considered different dynamic ranges (0.1, 0.9). Three benchmark databases, Cohn-Kanade, JAFFE, and In-house, are used for all experimentation. The five best machine learning algorithms are used for classification purposes. Experimental results show that SVM and MLP classifiers with wavelet and curvelet-based coefficients yield better results for emotion recognition. We can conclude that Wavelet coefficients-based features perform well, especially in the presence of Gaussian noise and illumination changes for facial expression recognition.

Keywords: Cohn-Kanade, Curvelet transform, Facial expression recognition, JAFFE, MLP, SVM, Wavelet transform.

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