Emotion Recognition from Facial Expressions Using Frequency Domain Techniques

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 264)

Abstract

An emotion recognition system from facial expression is used for recognizing expressions from the facial images and classifying them into one of the six basic emotions. Feature extraction and classification are the two main steps in an emotion recognition system. In this paper, two approaches viz., cropped face and whole face methods for feature extraction are implemented separately on the images taken from Cohn-Kanade (CK) and JAFFE database. Transform techniques such as Dual – Tree Complex Wavelet Transform (DT-CWT) and Gabor Wavelet Transform are considered for the formation of feature vectors along with Neural Network (NN) and K-Nearest Neighbor (KNN) as the Classifiers. These methods are combined in different possible combinations with the two aforesaid approaches and the databases to explore their efficiency. The overall average accuracy is 93% and 80% for NN and KNN respectively. The results are compared with those existing in literature and prove to be more efficient. The results suggest that cropped face approach gives better results compared to whole face approach. DT-CWT outperforms Gabor wavelet technique for both classifiers.

Keywords

Frequency Domain Feature Extraction Classification DT-CWT Gabor Wavelet Neural network KNN 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluruIndia
  2. 2.CTSChennaiIndia

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