Existing facial expression recognition techniques analyze the spatial and temporal information for every single frame in a human emotion video. On the contrary, we create the Emotion Avatar Image (EAI) as a single good representation for each video or image sequence for emotion recognition. In this paper, we adopt the recently introduced SIFT flow algorithm to register every frame with respect to an Avatar reference face model. Then, an iterative algorithm is used not only to superresolve the

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... I representation for each video and the Avatar reference, but also to improve the recognition performance. Subsequently, we extract the features from EAIs using both Local Binary Pattern (LBP) and Local Phase Quantization (LPQ). Then the results from both texture descriptors are tested on the Facial Expression Recognition and Analysis Challenge (FERA2011) data, GEMEP-FERA dataset. To evaluate this simple yet powerful idea, we train our algorithm only using the given 155 videos of training data from GEMEP-FERA dataset. The result shows that our algorithm eliminates the personspecific information for emotion and performs well on unseen data.