Facial Expression Recognition in Infrared Imaging Using HAPNet Segmentation and Hybrid VGG16-SVM Classifier

Recognition of Human Face expression is the most significant and challenging societal interaction tasks. Humans often convey their feelings and intentions through their facial expressions in a natural and honest manner, nonverbal communication is mostly characterized by facial expressions. Various approaches for classifying emotions and facial recognition have been established to enhance the accuracy of face recognition in the infrared images. Significant issues of recent deep FER systems include overfitting due to insufficient training data as well as expression-unrelated variables such as identification bias, head posture, and illumination. To address these challenges, the proposed model implemented a method for detecting facial expression using HAPNet segmentation and hybrid VGG16 with SVM classifier. At first, pre-processed the images using an optimized Difference of Gaussians (DOG) filter for enhancing the edges of the image and the Artificial Gorilla Troops Optimization Algorithm (GTO) is used to select the kernel size based on the maximum PSNR. Segmentation is the next step for segmenting the face using the Hybrid, Asymmetric, and Progressive Network (HAPNet) method. Landmark is detected based on Multi-Task Cascaded Convolutional Networks (MTCNN) for identifying the location of the mouth eyes, and nose. The last step is to categorize the seven emotions which are happy, sad, disgusted, surprised, angry, fearful, and neutral on faces using the hybrid VGG16 with Support Vector Machine (SVM) algorithm. The effectiveness of the proposed methodology is evaluated using the metrics of accuracy is 96.6%, positive predictive value is 93.08%, hit rate of 95.2%, selectivity of 92.5%, negative predictive value of 95.8%, and f1-score of 94.49%. Experiments on the database illustrates that the proposed approach performs better than conventional techniques for accurately identifies the expressions on the face in the thermal images.