A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification

Abstract

In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK\u2009+), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and Man–Machine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods.