Enhanced Real-Time Facial Expression Recognition Using Deep Learning
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2024, Vol 3, Issue 1
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.
Authors and Affiliations
Hafiz Burhan Ul Haq,Waseem Akram,Muhammad Nauman Irshad,Amna Kosar,Muhammad Abid
Keywords
Related Articles
- EP ID EP731901
- DOI https://doi.org/10.56578/ataiml030103
- Views 52
- Downloads 2
Comparative Analysis of Mortality Predictions from Lassa Fever in Nigeria: A Study Using Count Regression and Machine Learning Methods
In Sub-Saharan Africa, particularly in Nigeria, Lassa fever poses a significant infectious disease threat. This investigation employed count regression and machine learning techniques to model mortality rates associated...
Enhancing Session-Based Recommendations with Popularity-Aware Graph Neural Networks
Real-time and reliable recommendations are essential for anonymous users in session-based recommendation systems. Graph neural network-based algorithms are attracting more researchers due to their simplicity and efficien...
Multi-Variable Time Series Decoding with Long Short-Term Memory and Mixture Attention
The task of interpreting multi-variable time series data, while also forecasting outcomes accurately, is an ongoing challenge within the machine learning domain. This study presents an advanced method of utilizing Long S...
A Comparative Study on AI-Based Algorithms for Cost Prediction in Pharmaceutical Transport Logistics
Pharmaceutical transport logistics, especially in humanitarian and hospital contexts, is becoming increasingly essential with a growing need to monitor associated costs. In Morocco, however, studies focusing on the cost...
Hierarchical Aggregate Assessment of Multi-Level Teams Using Competency Ontologies
It is complex to assess multi-level hierarchical teams, because the solution needs to organize their rapid dynamic adaptation to perform operational tasks, and train team members without sufficient competencies, skills a...