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Emotion-aware mobile applications have been increasing due to their smart features and user acceptability. To realize such an application, an emotion recognition system should be in real time and highly accurate. As a mobile device has limited processing power, the algorithm in the emotion recognition system should be implemented using less computation. In this paper, we propose an emotion recognition with high performance for mobile applications. In the proposed system, facial video is captured by an embedded camera of a smart phone. Some representative frames are extracted from the video, and a face detection module is applied to extract the face regions in the frames. The Bandlet transform is realized on the face regions, and the resultant subband is divided into non-overlapping blocks. Local binary patterns' histograms are calculated for each block, and then are concatenated over all the blocks. The Kruskal-Wallis feature selection is applied to select the most dominant bins of the concatenated histograms. The dominant bins are then fed into a Gaussian mixture model-based classifier to classify the emotion. Experimental results show that the proposed system achieves high recognition accuracy in a reasonable time.