International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024
www.irjet.net
p-ISSN: 2395-0072
EXPLORING EMOTIONS OF DECIPHERING FACIAL EXPRESSIONS FOR EMOTION RECOGNITION WITH DEEP LEARNING Kuldeep Tripathi1, Dipti Ranjan Tiwari2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.1.Emergence of Deep Learning Abstract - The ability to understand and interpret human emotions based on facial expressions is a fundamental aspect of human communication and connection. In this research paper, we delve into the effectiveness of utilizing deep learning techniques for accurately identifying emotions from facial cues. Through the utilization of sophisticated neural network structures, particularly Convolutional Neural Networks (CNNs), our goal is to enhance the accuracy and dependability of emotion recognition systems. Our study involves assessing the performance of a variety of deep learning models on established datasets, shedding light on the strengths and weaknesses of each method. The results clearly show that deep learning significantly boosts the precision of emotion recognition in comparison to traditional approaches. Furthermore, we tackle the challenges posed by the diversity of facial expressions seen across different individuals and situations. This research contributes to the advancement of more intuitive and interactive human-computer interaction systems, with potential applications in fields like mental health evaluation, security measures, and automated customer service.
In the past, emotion recognition systems have traditionally utilized methods like Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Active Appearance Models (AAMs) to capture facial characteristics. These techniques involved extracting specific features from the face, which were then input into classifiers such as Support Vector Machines (SVMs) or k-Nearest Neighbours (k-NN) for analysis. Despite some level of success, these approaches faced challenges when dealing with changes in lighting, different poses, and obstructions that could affect the accuracy of the emotion detection process.
1.2.Emergence of Deep Learning Deep learning, particularly Convolutional Neural Networks (CNNs), has proven to be superior to traditional techniques in a wide range of tasks involving images. CNNs have the ability to automatically learn intricate feature representations in a hierarchical manner, which makes them ideal for handling complex tasks such as facial emotion recognition. Some of the most notable CNN architectures include AlexNet, VGGNet, and ResNet. These architectures have significantly enhanced the accuracy and efficiency of image classification, pushing the boundaries of what is possible in the field of computer vision.
Key Words: Emotion recognition, Facial expressions, Deep learning, Convolutional neural networks (CNNs), Recurrent neural networks (RNNs)
1.INTRODUCTION
2.SYSTEM COMPONENTS AND FUNCTIONALITIES
Facial expression recognition plays a crucial role in the realms of computer vision and affective computing. The human face serves as a rich tapestry of emotions, enabling the identification of a wide range of feelings such as happiness, sadness, anger, and surprise. Historically, the process of recognizing facial emotions relied heavily on handcrafted features and conventional machine learning methods, necessitating a profound comprehension of the domain and encountering challenges when faced with diverse datasets. As technology advances, new approaches such as deep learning and neural networks have emerged, revolutionizing the field of facial expression recognition by enhancing accuracy and adaptability to various contexts. The evolution of this research area not only sheds light on the complexity of human emotions but also paves the way for innovative applications in areas like human-computer interaction, healthcare, and psychology.
© 2024, IRJET
|
Impact Factor value: 8.226
2.1.Input Module The main duty in this position involves the collection of input data, which usually comprises images or video frames showcasing human faces. This data is sourced from a range of places, such as webcam streams, video recordings, and image folders. Following the data collection phase, the subsequent step is focused on refining and organizing the data for further analysis. This process encompasses activities like identifying faces, aligning them properly, standardizing the images, and adjusting their sizes to guarantee that the data is primed for examination and deployment across different applications.
|
ISO 9001:2008 Certified Journal
|
Page 1112