Emotional quotient (EQ) is one of the main factors determining the outcome of a learning process. A cognitive-affective states that usually appear during a learning process are bored, confuse, and excited/enthusiastic. Emotion state can be detected by identifying human facial expressions. Here, Principal Component analysis (PCA) and Gabor features extract salient information from facial expression database. Each feature space obtained from these methods is then classified using multi-class Support Vector Machine (SVM) with two cross-validation methods, Holdout and 10-fold cross validation. Experiment results show that classification process using Gabor features and 10-fold cross validation of multi-class SVM give the best accuracy rate.