Two Dimensional Principal Component Analysis (2dpca) For Human Facial Expression Recognition

Résumé: Human facial expression recognition by a machine can be described as an interpretation of human facial characteristics via mathematical algorithms. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information. By observing face, one can decide whether a man is happy, sad, thinking and so on. Recognition the expression of a man can help in many of the areas like in the field of medical science where a doctor can be altered when a patient is in severe pain. In this paper, we propose the Radial Basis Function Neural Network (RBF-NN) model to apply for image classification. Firstly, images are projected to difference spaces by two-dimensional principal components analysis (2DPCA); we propose an algorithm for facial expression recognition, which can classify the given image into one of the seven basic facial expression categories (Happiness, Sadness, Fear, Surprise, Anger, Disgust and neutral). Our program has been tested using JAFFE Database, available at http://www.kasrl.org/jaffe.html consisting 213 images posed by 10 Japanese female models. We obtain a recognition rate equal to 96.42%.

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