An Improved Facial Expression Recognition Method Using Combined Hog and Gabor Features

Lately, face recognition technology has been a significant study and a topic for generations. It remains a difficult task because of the variability of wide interclass. The subject of facial expression recognition is addressed in this research using a practical method. This method can recognize the human face and it is various features such as the eyes, brows, and lips. The motions or deformations of the face muscles are the cause of facial expressions. In addition, computer vision tasks such as texture recognition and categorization are commonly used. Furthermore, feature extraction basically discovers groups of features that demonstrate an image of visual texture. It is a critical phase to complete the operation. This work extracts features utilizing Histogram of Oriented Gradients (HOG) and Gabor approaches and then combines extracted features to improve the accuracy of facial expression detection. The derived features were particularly sensitive to object deformations. Later on, the classification of facial expression is handled using (Support Vector Machine) SVM. Analyze the proposed approach on FER 2013 data to see how well it performs. The proposal has a categorization rate of 63.82% on average. The proposed technique determines the comparable classification accuracy as shown in experimental findings. To improve this work it is planned to use deep features and combined them with HOG or Gabor, as well as to show the efficiency of the work it can be implemented with more datasets such as the JAFFE database.