A comparison of several classifiers for eye detection on emotion expressing faces

This paper presents a study of three classification methods applied on natural images with the goal of detecting the eye regions. The challenge that we aim to solve is finding the eyes on faces expressing emotions - this is extremely difficult because the shape of the facial features changes drastically when the subject goes through an emotional state. We attempt to solve this challenge by looking for eye patterns in an image and categorizing them using neural network classification approaches. The image features used are integral projections encoded by TESPAR method, thus reducing the dimensionality of the problem to vectors of 60 elements - we observed that the network classification ability becomes more robust on input samples computed in this manner and the computational complexity is lowered, such that the system gets closer to making real-time decisions. The paper presents experimental results of three classifiers (SVM, Naïve Bayes and MLP) obtained on the Cohn-Kanade reference database. The measurements show that by tuning each classifier properly and by performing a post-processing step, we can obtain accuracies of over 90%.