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This paper details the authors' efforts to push the baseline of emotion recognition performance on the Geneva Multimodal Emotion Portrayals (GEMEP) Facial Expression Recognition and Analysis database. Both subject-dependent and subject-independent emotion recognition scenarios are addressed in this paper. The approach toward solving this problem involves face detection, followed by key-point identification, then feature generation, and then, finally, classification. An ensemble of features consisting of hierarchical Gaussianization, scale-invariant feature transform, and some coarse motion features have been used. In the classification stage, we used support vector machines. The classification task has been divided into person-specific and person-independent emotion recognitions using face recognition with either manual labels or automatic algorithms. We achieve 100% performance for the person-specific one, 66% performance for the person-independent one, and 80% performance for overall results, in terms of classification rate, for emotion recognition with manual identification of subjects.