Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 24, Pages: 1-9
Sahar Zafar Jumani1*, Fayyaz Ali2, Subhash Guriro1, Irfan Ali Kandhro1, Asif Khan1 and Adnan Zaidi3
1Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan;[email protected], [email protected], [email protected], [email protected] 2Department of Computer Science, Sir Syed University of Engineering and Technology, Karachi, Pakistan; [email protected]
3Department of Computer Science, Muhammad Ali Jinnah University, Karachi, Pakistan; [email protected]
*Author for correspondence
Sahar Zafar Jumani
Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan. Email: [email protected]
Objectives: A new method is introduced in this study for Facial expression recognition using FER2013 database consisting seven classes consisting (Surprise, Fear, Angry, Neutral, Sad, Disgust, Happy) in past few decades, Exploration of methods to recognize facial expressions have been active research area and many applications have been developed for feature extraction and inference. However, it is still challenging due to the high-intra class variation. Methods/Statistical Analysis: we deeply analyzed the accuracy of both handcrafted and leaned aspects such as HOG. This study proposed two models; (1) FER using Deep Convolutional Neural Network (FER-CNN) and (2) Histogram of oriented Gradients based Deep Convolutional Neural Network (FER-HOGCNN). the training and testing accuracy of FER-CNN model set 98%, 72%, similarly Losses were 0.02, 2.02 respectively. On the other side, the training and testing accuracy of FER- HOGCNN model set 97%, 70%, similarly Losses were 0.04, 2.04. Findings: It has been found that the accuracy of FER- HOGCNN model is good overall but comparatively not better than Simple FER-CNN. In dataset the quality of images are low and small dimensions, for that reason, the HOG loses some important features during training and testing. Application/Improvements: The study helps for improving the FER System in image processing and furthermore, this work shall be extended in future, and order to extract the important features from images by combining LBP and HOG operator using Deep Learning models.
Keywords: Deep Learning, Emotion Recognition, Facial Expression, CNN, FER, HOG
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