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Spontaneous expression recognition refers to recognizing non-posed human expressions. In literature, most of the existing approaches for expression recognition mainly rely on manual annotations by experts, which is both time-consuming and difficult to obtain. Hence, we propose an unsupervised framework for spontaneous expression recognition that preserves discriminative information for the videos of each expression without using annotations. Initially, a large Gaussian mixture model called universal attribute model (UAM) is trained to learn the attributes of various expressions implicitly. Attributes are the movements of various facial muscles that are combined to form a particular facial expression. Then a concatenated mean vector called the super expression-vector (SEV) is formed by using a maximum a posteriori adaptation of the UAM means for each expression clip. This SEV contains attributes from all the expressions resulting in a high dimensional representation. To retain only the attributes of that particular expression clip, the SEV is decomposed using factor analysis to produce a low-dimensional expression-vector. This procedure does not require any class labels and produces expression-vectors that are distinct for each expression irrespective of high inter-actor variability present in spontaneous expressions. On spontaneous expression datasets like BP4D and AFEW, we demonstrate that expression-vector achieves better performance than state-of-the-art techniques. Further, we also show that UAM trained on a constrained dataset can be effectively used to recognize expressions in unconstrained expression videos.

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In this paper, a new technique for facial expression recognition is proposed which uses the statistical feature of the whole face and classify the expression using neural network classifier. When the face image is input, region of interest (ROI) is being obtained to evaluate the statistical feature of the face. Using these, features we classify the face into one of the seven different expressions by using multi label Back Propagation neural network classifier. To demonstrate the proposed recognition technique we use JAFFE facial database and the whole program is being implemented in MATLAB 7.0.