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This work reports a study about the use of Gabor coefficients and coordinates of fiducial (landmark) points to represent facial features and allow the discrimination between photogenic and non-photogenic facial images, using neural networks. Experiments have been performed using 416 images from the Cohn-Kanade AU-Coded Facial Expression Database [1]. In order to extract fiducial points and classify the expressions, a manual processing was performed. The facial expression classifications were obtained with the help of the Action Unit information available in the image database. Various combinations of features were tested and evaluated. The best results were obtained with a weighted sum of a neural network classifier using Gabor coefficients and another using only the fiducial points. These indicated that fiducial points are a very promising feature for the classification performed.

Neural Network Classification of Photogenic Facial Expressions Based on Fiducial Points and Gabor Features