Impact of self adaptive-elephant herding optimization towards neural network for facial emotion recognition

Abstract

FACIAL expression is one of the most efficient, universal and fundamental indicators to identify their emotions and intentions in humans. Various experiments have already been performed on automatic Facial Emotion Recognition (FER) owing to useful significance in medical diagnosis, stress monitoring for drivers, sociable robots, and other human-computer interface devices. Here, this proposed framework consists of two processes namely; “(i) proposed feature extraction and (ii) classification”. Here, a major novelty relies in the initial phase (i.e. feature extraction phase), where the Proposed Local Vector Pattern (Proposed- LVP) based features are extracted. In addition to the proposed-LVP, the other Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrix (GLCM) based features are also extracted. Besides, the Principal Component Analysis (PCA) method is used for reducing the dimension of the features. Further, they are subjected to classification process, where Optimized Neural Network (NN) is used. More particularly, a new Improved Elephant Herding Optimization (EHO) model termed as Self Adaptive-EHO (SA-EHO) is used to train the NN model via selecting the optimal weights. At last, the proposed work performance is computed over the other traditional systems with respect to the positive measures like “accuracy, sensitivity, specificity and precision”; negative measures like “False Positive Rate (FPR), False Negative Rate (FNR) and False Discovery Rate (FDR)”; other measures like “Negative Predictive Value (NPV), F1-score and Matthew’s Correlation Coefficient (MCC)”, respectively.