A comparative analysis of filter and wrapper approaches of feature selection has been presented for the audio, visual and audio-visual human emotion recognition. A large set of audio and visual features were extracted, followed by speaker normalization. In filter approach, feature selection was performed using the Plus l-Take Away r algorithm based on Bhattacharyya distance criterion. In wrapper method, features were selected based on their classification performance using support vector machine (SVM) classifier. Finally, an SVM classifier was used for human emotion recognition. The filter approach provided a slight better performance in comparison to wrapper approach for seven emotions on SAVEE database.