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Facial expression recognition (FER) from video is an essential research area in the field of human computer interfaces (HCI). In this work, we present a new method to recognize several facial expressions from time sequential facial expression images. To produce robust facial expression features, enhanced independent component analysis (EICA) is utilized to extract locally independent component (IC) features which are further classified by Fisher linear discriminant analysis (FLDA). Using these features, discrete hidden Markov models (HMMs) are utilized to model different facial expressions such as joy, anger, and sad. Performance of our proposed FER system is compared against four other conventional feature extraction approaches (i.e., PCA, PCA-FLDA, ICA, and EICA) in conjunction with the same HMM scheme. The experimental results using the Cohn-Kanade database of facial expression videos show that our proposed system yields much improved recognition rate reaching the mean recognition rate of 93.23% whereas the conventional methods yield 82.92% at best.