International Research Journal of Engineering and Technology (IRJET) Volume: 11 Issue: 04 | Apr 2024
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
HAND GESTURE RECOGNITION SYSTEM Jean Jacob1, Abhimanyu VM2, Ashin Girish3, Ashly Shaji4, Bhagyalakshmi Biju5 1Assistant professor, Dept. of Computer Science and Engineering, Sree Narayana Gurukulam College Of
Engineering, Kadayirupppu, Ernakulam, Kerala, India 2,3,4,5UG student, Dept. of Computer Science and Engineering, Sree Narayana Gurukulam College Of Engineering,
Kadayirupppu, Ernakulam, Kerala, India ------------------------------------------------------------------------***---------------------------------------------------------------------
Abstract- Hand gesture recognition systems have gained
for training, and incorporating advanced neural network architectures. The practical applications of hand gesture recognition are diverse, ranging from gaming and virtual reality experiences to hands-free control in smart environments. As technology continues to advance, hand gesture recognition systems are expected to play a pivotal role in enhancing user interfaces and facilitating more natural and immersive interactions with machines.
significant attention in recent years due to their potential applications in human-computer interaction, virtual reality, robotics, and various other fields. This paper presents a comprehensive overview of a HGRS designed to accurately interpret and respond to HHM. The proposed system leverages advanced computer vision techniques, machine learning algorithms, and deep neural networks to achieve robust and realtime hand gesture recognition. The process begins with hand detection using state-of-the-art algorithms, followed by hand tracking to ensure continuous monitoring of gestures. The system then extracts relevant features from the hand movements, considering factors such as hand shape, orientation, and motion dynamics. To enhance the accuracy and adaptability of the system, a machine learning model is trained on a diverse dataset of hand gestures. Transfer learning techniques are employed to fine-tune the model on specific gesture categories, allowing the system to recognize a wide range of gestures with high precision.
1.2 OBJECTIVE The main goal of a HGRS is to create technology that lets computers understand and respond to HHM accurately and quickly. This involves making sure the system can recognize a variety of hand gestures, work in real-time, handle different environments, and be easy for users to interact with. The system should also be adaptable to different devices and capable of learning new gestures over time. The practical applications include improving how people interact with computers, virtual reality experiences, gaming, and other areas.
I. INTRODUCTION
II.
1.1 GENERAL BACKGROUND
The purpose of a literature review is to, as the name suggests, “review” the literature surrounding a certain topic area. The word “literature” means “sources of information” or “research.” The literature will inform us about the research that has already been conducted on our chosen subject.
Hand gesture recognition is a technology that enables computers to interpret and respond to HHM, allowing for intuitive and natural interaction between humans and machines. This field has gained increasing prominence in recent years due to its applications in diverse areas, including human-computer interaction, virtual reality, augmented reality, robotics, and healthcare. The primary goal of hand gesture recognition systems is to accurately interpret the gestures made by users, translating them into meaningful commands or actions. These systems typically employ a combination of computer vision, image processing, and machine learning techniques. Computer vision algorithms are utilized to detect and track the movement of hands in images or videos, while machine learning models, often based on deep neural networks, are employed to recognize specific gestures based on learned patterns and features. The challenges in hand gesture recognition include variations in hand poses, lighting conditions, and the need for real-time processing. Researchers and developers address these challenges by exploring innovative algorithms, leveraging large datasets
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Impact Factor value: 8.226
LITERATURE SURVEY
Paper [1]. "Real-Time Hand Gesture Recognition Using Convolutional Neural Networks" by C. Cao, Y. Wu, Z. Xu: In the dynamic landscape of human-computer interaction, the paper by C. Cao, Y. Wu, and Z. Xu, titled "Real-Time Hand Gesture Recognition Using Convolutional Neural Networks," stands as a pivotal contribution. The authors address the critical need for instantaneous responsiveness in computing systems, particularly in scenarios requiring realtime interaction. The key innovation lies in the strategic utilization of Convolutional Neural Networks (CNNs), a class of deep learning models renowned for their proficiency in imagerelated tasks. The paper introduces a system that leverages CNNs to accurately and swiftly recognize dynamic hand gestures. CNNs, with their
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