Real-time Gesture and Face Recognition
Developed high-accuracy systems for real-time hand gesture and face recognition.
A suite of high-performance computer vision systems designed for real-time applications, including identifying individuals through facial recognition and interpreting hand movements for touchless control.
This project showcases a collection of practical applications built using advanced computer vision and deep learning techniques. The primary focus was on developing real-time systems that can interpret visual data from a camera feed, including a highly accurate Face Recognition system. By leveraging Convolutional Neural Networks (CNNs), this system was trained to identify individuals with an impressive 88% accuracy on challenging real-world facial datasets, demonstrating its potential for security and personalization applications. In addition to facial recognition, a robust hand gesture recognition system was developed. This component can identify over five unique hand gestures in real-time, translating physical movements into digital commands with 95% accuracy. This capability opens the door for innovative human-computer interaction, allowing users to control applications, navigate menus, or interact with smart devices without physical touch. The system is designed to be responsive and reliable, even under varying lighting conditions. The project also delves into the realm of Natural Language Processing by fine-tuning a text sentiment analysis model. By training the model on a custom-labeled dataset, a significant 15% performance improvement over baseline models was achieved. This demonstrates a holistic understanding of AI, applying cutting-edge concepts from both computer vision and NLP to build practical, industry-oriented solutions that are aligned with real-world use cases and challenges.
- Face recognition with 88% accuracy on real-world datasets.
- Hand gesture recognition for over 5 unique gestures with 95% accuracy.
- Real-time processing from a standard camera feed.
- Built with deep learning models like Convolutional Neural Networks (CNNs).
- Includes a fine-tuned sentiment analysis model for text processing.
- Enables secure and personalized user experiences through biometrics.
- Offers innovative, touch-free methods for human-computer interaction.
- Demonstrates the ability to build and deploy high-accuracy AI models.
- Applicable to a wide range of security, control, and interactive systems.
Applications include secure access control systems, interactive digital kiosks, assistive technology for users with disabilities, and creating more intuitive interfaces for gaming or smart home devices.
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