Performance Analysis of Feature Extraction Techniques for Facial Expression Recognition

Facial Expression Recognition is a vital topic for research in current scenario which has many applications as machine based HR interviews and human-machine interaction. Facial Expression recognition is applied for identification of person using face of a person. Researchers have proposed many research techniques for facial expression recognition but still accuracy, illumination and occlusion are the research issues which have to improve. So research issues are to improve recognition rate by improving the pre-processing of datasets, improving the feature extraction method and using the best classifier for facial expression recognition. Feature extraction is the key step on which recognition rate depends for facial expression recognition. The purpose of this research work is to analysis of different feature extraction technique in frequency domain as Gabor filter, Discrete Wavelet Transform and Discrete Cosine Transform feature extraction technique. Accuracy is the key research issue in facial expression recognition which is measured in term of Recognition rate.