Human Face Emotions Recognition from Thermal Images Using DenseNet

Abstract

In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

A combined method based on CNN architecture for variation-resistant facial recognition

“…Also, we used the SOFTMAX optimization algorithm in the output layer to perform a multi-class classification to classify the data into several categories. Its main role is to normalize the scores of each output class into a probability distribution that represents the probability of each class being the correct prediction using the following equation: (9) Z is a vector of real numbers that represents a score for a particular class j and k is the class number. Fig.…”

“…Facial recognition is a technology that allows the identification of a person by analyzing and comparing unique features of the face, such as the shape of the nose, the distance between the eyes, or the facial lines. This technology is increasingly used in various fields such as security [1,2], Human face recognition and age estimation [3], video surveillance [4], gender identification from an image [5], biometric identification [6] or individual identification [7][8][9]. However, the presence of variance that can occur in several forms (lighting, orientation, pose, accessories, etc.)…”