Facial detection plays a pivotal role in computer vision applications, spanning facial recognition, surveillance, and augmented reality. This study delves into the efficacy of the Hybrid method, combining the Histogram of Oriented Gradients (HOG) algorithm with a Support Vector Machine (SVM) classifier for robust facial detection. HOG emphasizes extracting pertinent features, focusing on the shape and texture of objects within images. These features serve as inputs for an SVM classifier, training it to distinguish faces from other objects. The research aims to create a precise facial detection system adaptable to diverse conditions, encompassing variations in orientation, lighting, and shadows. The emphasis lies on optimizing the HOG-SVM approach to achieve exceptional performance without compromising processing efficiency. Experimental evaluations, conducted on the LFW and Wider datasets, demonstrate promising results, showcasing the superior accuracy of our HOG-SVM approach compared to recent methodologies.

Automated sensing of the student's engagement in an e-learning system from emotional expressions remains a challenging problem due to varying conditions during the lecture. Such recognition and detection systems improve the teaching experience and efficiency by providing valuable feedback. Emotional expressions are expressed through non-verbal and verbal human emotional/behavior. More investigations are needed in this domain to carry out the learning process. Deep multi-task learning has been successfully employed in many real-world large-scale applications such as recognition systems. In this paper, we propose a novel education level state system to determine the student engagement level in an e-learning environment. The proposed approach is based on a hybrid deep multi-task learning technique. Soft and hard parameters are fused to achieve the best prediction. The performance of this system is evaluated on three facial expression benchmark datasets acquired in non-controlled environments. We validate the proposal using multi-input and mixed data to meet the relevant challenges.

Recognizing human expression is one of the most popular problems in the Human-Computer Interaction field. Facial Expression Recognition present a great challenge in a wide variety of areas due to varying conditions of the image, which influences expression recognition and makes this task a complex problem. The main difficulties depend on the irregular nature of the human face and the different conditions such as orientation, light and shadows. Lately, Deep learning obtained more attention as an intelligent technology to achieve robustness and offer best performance of expression recognition. Further investigations are still needed in this field in order to make the recognition process very efficient. For that, we present in this paper a new Convolutional Neural Networks model enhancing with pre-processing stages to recognize seven classes (six basic expressions and one neutral). Our approach contains two phases: normalization, and expression recognition. The result can achieve high accuracy compared to recent works with the popular facial expression databases such as CK+, JAFFE, and FER-2013.