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Proposed Systems Network Model [10]
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For a computer, identification of human emotion from a still image of the human face is a complex, challenging, and heavily calculative task. Classification of human emotion is done by using a different combination of convolutional neural networks (CNN) that task is known as Facial Emotion Recognition (FER). CNN model is achieved by training and te...
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Emotions play a crucial role in shaping human thoughts, behavior, and feelings. Consequently, the task of recognizing emotions from facial images has attained significant attention because of its applications across various areas. Traditional algorithms have often proven inadequate in meeting real-time human needs, leading to the emergence of machine learning and deep learning algorithms as more effective alternatives. Existing researchers of computer vision and affective computing have primarily concentrated on enhancing the classification accuracy of benchmark datasets. In line with these advancements, the main focus of the study is to evolve a specialized Neural Network based model which is competent in accurately classifying seven distinct human facial emotions. The model is meticulously trained and rigorously tested using both laboratory-controlled with an accuracy of 98% and 53% over uncontrolled environment images without using any sort of pre-processing and also ensuring model robustness across different scenarios.
Emotions are a big part of human life. Humans experience a variety of emotions on a daily basis, and each emotion has a unique impact on how the face moves. As a consequence, vast research has been conducted in the recent years for improvement in facial expression recognition (FER) system. However, a deep survey on progression of research work in FER systems is still lacking. The study primarily emphasizes several emotional classifications and presents an in depth survey of on progression made in FER systems. The literature survey on this topic reveals that significant work has been done on images gathered in a controlled environment to study facial expression recognition (FER). However, a few already-developed models produced positive outcomes for a real-world environment. Hence, the developed models are not appropriate. Overall, it is expected that the study may guide researchers for the future and serve as an inspiration to them for bringing appreciable changes in the discipline of facial expression recognition. Finally, the deep survey is concluded with the future scope of the research as well as the area of applications in which this technology can play a vital role.
