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.