Multi-Featured Extraction and Convolution Neural Network Based SVM for Automatic Facial Emotion Recognition - Regina, M; Josephine, MS; Jeyabalraja, V (International Journal of Computer Sciences and Engineering, 2025)
Human emotions are mental states of feelings that are exposed unconsciously and followed by physical changes in their facial muscles which entail the expressions on the face. Certain emotions commonly expressed by human are happiness, sadness, anger, fear, disgust, surprise, and neutral. For a non-verbal communication, facial expression plays a vital role since it appears because of inner core feelings of a person that reflects on the faces. For the automatic recognition of facial emotions, many methods are used such as Artificial Neural networks, Neuro-fuzzy, Wavelet transformation, etc. However, the existing methods take more time for data classification, low accuracy in the optimization process and high level of error rate. To overcome these concerns, this paper depicts an amphibious operation of Multi Support Vector Machine (SVM) with the Convolutional Neural Networks (CNN). Initially, the characteristics of the pre-processed face image are efficiently extracted by using Local Binary Pattern (LBP), Principal Component Analysis (PCA) and Gray Level Occurrence Matrix (GLCM). In this model, CNN works as a trainable feature extractor, and Multi-SVM performs as a recognizer. The proposed system`s performance is analyzed with various human faces using the MATLAB tool. The results prove that the proposed method surpasses the earlier methods regarding high accuracy with low computation time and low error rate.