An Efficient Hybrid Technique Of Feature Extraction For Facial Expression Recognition Using Adaboost Classifier

Facial Expression Recognition is widely used for designing of human-machine interface. The research issue of Facial Expression Recognition is to select the features which are required to represent a Facial Expression. In this paper we proposed a hybrid method of feature extraction using Discrete Cosine Transform, Wavelet Transform, Gabor Filter and Gaussian distribution to select the distinguished feature for improving the recognition rate of facial expression. JAFFE dataset are used for recognition of different seven expressions: anger, disgust, fear, happiness, sadness, surprise, neutral in Experiments and the result of proposed hybrid technique is compared with results of individual Feature Extraction Techniques as DCT based technique, Wavelet transform based technique, Gabor filter based technique & Gaussian Derivatives based technique which shows that Recognition Rate can be improved by combining distinguished optimum features of DCT, Gabor Filter, Wavelet Transform and Gaussian Distribution in a feature vector for facial expression recognition. KeywordsHybrid, Facial Expression Recognition, Gesture, DCT, Wavelet, Gabor Filter, Gaussian distribution.