Feature Extraction based on Canonical Correlation Analysis using FMEDA and DPA for Facial Expression Recognition with RNN

A feature extraction method for Facial Expression Recognition Systems is proposed based on CCA using FMEDA and DPA. For proper classification of expression it has been trained with Recurrent neural network. The Cohn Kanade Extensive, ORL and Yale databases are used in this paper. The images have been preprocessed using image normalization and then contrast limited adaptive histogram equalization to remove the illumination variance and noises. After down-sampling, the dimensions with factor data is provided to Canonical Correlation Analysis that finds the linear combinations among two sets of variables for determining correlation with each other. DPA and FMEDA have been used to extract features from specially low frequency coefficients of the image as some of the low frequency coefficients have more discrimination power compared to others and by extracting those features a higher true recognition rate can be achieved. The results provided in this paper show the advantage of this method compared to other methods like Principal Component Analysis and Linear Discriminant Analysis which uses between and within class scatter matrices and try to maximize the discrimination in transformed domain. This method searches for best discrimination features in transformed domain. Dimensionally reduced data is provided to recurrent neural network for training purpose. Meanwhile, the proposed method is more robust and effective compared to other methods in this field.