Human Emotion Recognition System Using Optimally Designed SVM With Different Facial Feature Extraction Techniques

摘要

This research aims at developing "Humanoid Robots" that can carry out intellectual conversation with human beings. The first step in this direction is to recognize human emotions by a computer using neural network. In this paper all six universally recognized basic emotions namely angry, disgust, fear, happy, sad and surprise along with neutral one are recognized. Various feature extraction techniques such as Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT), Singular Value Decomposition (SVD) are used to extract the useful features for emotion recognition from facial expressions. Support Vector Machine (SVM) is used for emotion recognition using the extracted facial features and the performance of various feature extraction technique is compared. Authors achieved 100% recognition accuracy on training dataset and 94.29% on cross validation dataset.