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.
