面部表情识别
计算机科学
面部识别系统
面部表情
人工智能
模式识别(心理学)
计算机视觉
作者
K C Tejaswi,D Mokshith,Sai Pradeep E,Ch Mahesh Kumar,Manoj Kumar K
标识
DOI:10.1109/rmkmate59243.2023.10369028
摘要
This study presents a facial expression recognition system that utilizes the You Only Look Once (YOLO) object detection framework. The system leverages the capabilities of YOLO to detect and classify facial expressions accurately and efficiently.The main objective is to achieve efficient and real-time detection and classification of facial expressions. By utilizing the YOLO framework's object detection capabilities, the system can accurately locate and extract facial regions of interest for subsequent analysis. To train the system, a large dataset of labeled facial images representing various expressions, such as happiness, sadness, anger, fear, surprise, and neutral, is utilized. Deep learning techniques, including convolutional neural networks (CNNs), are employed to optimize the modified YOLO network's parameters, enhancing expression recognition accuracy.Experimental evaluation on benchmark facial expression datasets demonstrates the effectiveness and efficiency of the proposed YOLO-based facial expression recognition system. It surpasses existing approaches in terms of both accuracy and real-time performance, making it highly suitable for practical applications. The proposed facial expression recognition system based on the YOLO object detection framework demonstrates the capability to detect and classify facial expressions in real-time. This advancement opens up new possibilities in fields such as emotion detection, human-computer interaction, and affective computing. The approach not only improves accuracy but also addresses the crucial requirement for real-time processing, which is essential for various real-world applications. By leveraging the advantages of the YOLO framework, the system achieves a good balance between accuracy and speed, enabling efficient and effective facial expression analysis. With its promising results, the YOLO-based facial expression recognition system holds great potential for advancing fields that rely on accurate and real-time emotion analysis..
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