This paper presents a facial expression recognition algorithm based on segmentation of a face image into four facial regions (eyes-eyebrows, forehead, mouth and nose). In order to unify the different results obtained from facial region combinations, a modal value approach that employs the most frequent decision of the classifiers is proposed. The robustness of the algorithm is also evaluated under partial occlusion, using four different types of occlusion (half left/right, eyes and mouth occlusion). The proposed method employs sub-block eigenphases algorithm that uses the phase spectrum and principal component analysis (PCA) for feature vector estimation which is fed to a support vector machine (SVM) for classification. Experimental results show that using modal value approach improves the average recognition rate achieving more than 90% and the performance can be kept high even in the case of partial occlusion by excluding occluded parts in the feature extraction process.

An increasing number of psychological studies have demonstrated that the six basic expressions of emotions are not culturally universal. However, automatic facial expression recognition (FER) systems disregard these findings and assume that facial expressions are universally expressed and recognized across different cultures. Therefore, this paper presents an analysis of Western-Caucasian and East-Asian facial expressions of emotions based on visual representations and cross-cultural FER. The visual analysis builds on the Eigenfaces method, and the cross-cultural FER combines appearance and geometric features by extracting Local Fourier Coefficients (LFC) and Facial Fourier Descriptors (FFD) respectively. Furthermore, two possible solutions for FER under multicultural environments are proposed. These are based on an early race detection, and independent models for culture-specific facial expressions found by the analysis evaluation. HSV color quantization combined with LFC and FFD compose the feature extraction for race detection, whereas culture-independent models of anger, disgust and fear are analyzed for the second solution. All tests were performed using Support Vector Machines (SVM) for classification and evaluated using five standard databases. Experimental results show that both solutions overcome the accuracy of FER systems under multicultural environments. However, the approach which individually considers the culture-specific facial expressions achieved the highest recognition rate.