Enhanced Real-Time Facial Expression Recognition Using Deep Learning

Abstract

In the realm of facial expression recognition (FER), the identification and classification of seven universal emotional states, surprise, disgust, fear, happiness, neutrality, anger, and contempt, are of paramount importance. This research focuses on the application of convolutional neural networks (CNNs) for the extraction and categorization of these expressions. Over the past decade, CNNs have emerged as a significant area of research in human-computer interaction, surpassing previous methodologies with their superior feature learning capabilities. While current models demonstrate exceptional accuracy in recognizing facial expressions within controlled laboratory datasets, their performance significantly diminishes when applied to real-time, uncontrolled datasets. Challenges such as degraded image quality, occlusions, variable lighting, and alterations in head pose are commonly encountered in images sourced from unstructured environments like the internet. This study aims to enhance the recognition accuracy of FER by employing deep learning techniques to process images captured in real-time, particularly those of lower resolution. The objective is to augment the accuracy of FER in real-world datasets, which are inherently more complex and collected under less controlled conditions, compared to laboratory-collected data. The effectiveness of a deep learning-based approach to emotion detection in photographs is rigorously evaluated in this work. The proposed method is exhaustively compared with manual techniques and other existing approaches to assess its efficacy. This comparison forms the foundation for a subjective evaluation methodology, focusing on validation and end-user satisfaction. The findings conclusively demonstrate the method's proficiency in accurately recognizing emotions in both laboratory and real-world scenarios, thereby underscoring the potential of deep learning in the domain of facial emotion identification.

Decision Support System for Mobile Phone Selection Utilizing Fuzzy Hypersoft Sets and Machine Learning

“…This review of the literature integrates findings from several research disciplines to offer a thorough overview of decision-making [37][38][39][40] in the selection of a mobile phone. A comprehensive viewpoint can be obtained by comprehending consumer behavior, ideas surrounding technology adoption, the function of DSS, and similarities to agricultural decision-making [41][42][43]. Future studies can examine the changing dynamics of mobile phone selection and the influence of developing technologies on decision-making processes as technology develops [42][43][44][45].…”

Enhancing Educational Design Capabilities through Interactive Mobile and Adaptive Learning Platforms: An Empirical Study

“…This leads to a lack of precision and personalization in the recommendations provided by these systems [14][15][16][17]. Thus, refining matching algorithms and recommendation strategies in adaptive learning platforms, particularly those incorporating interactive mobile technologies, is crucial for enhancing educational design skills [18][19][20].…”

Analysis and Emotion Recognition of Educational Network New Media Images Based on Deep Learning

“…Related research shows that image content analysis and emotion recognition based on deep learning have achieved significant results in multiple fields. In the field of education, accurate image content analysis can automatically annotate educational resources, providing teachers and students with efficient retrieval and usage methods [9][10][11]; while emotion recognition can help teachers understand students' emotional states in real-time, adjust teaching strategies timely, and improve teaching effectiveness [12][13][14]. Therefore, in-depth research on the content analysis and emotion recognition of ENNM images has important theoretical value and practical significance.…”