Face Recognition in Smart Watch
The Android smartwatch face recognition application achieves 0.91 accuracy, enabling seamless face registration via a web portal along with robust local recognition. It enhances user interaction while delivering efficient and secure facial recognition capabilities.
Are you looking to develop an image processing & AI/ML project?
About Smart Watch
The Android smartwatch face recognition system offers offline facial recognition, allowing users to register and identify faces using a web portal combined with a locally trained machine learning model for high-precision results.
This system was built using Kotlin, Python, TensorFlow, Computer Vision, and other AI/ML technologies. By prioritizing privacy, performance, and future accuracy improvements, the solution ensures reliability for diverse use cases. The use of on-device AI eliminates dependency on external servers, improving user trust and operational resilience.
Moreover, the system supports ongoing enhancements and adaptability to new technologies, ensuring long-term value and scalability as a future-ready solution.
Features & Purpose Of
This Application
Emotion Detection through Facial Cues
The smartwatch is capable of recognizing facial emotions such as anger, happiness, sadness, smiling, and more, enhancing the emotional intelligence of wearable tech.
The Smart Watch captures facial emotions like anger, happiness, sadness, smiling and so on.
Computer Vision for Image Interpretation
Computer Vision algorithms allow the smartwatch to interpret and understand visual inputs from digital images or video frames, enabling accurate recognition and analysis.
Computer Vision for enabling computers to gain a high-level understanding from digital images or videos.
TensorFlow-Powered Model Training
The application uses TensorFlow to build and train machine learning models, with a specific focus on facial recognition tasks, ensuring scalable and accurate processing.
Application utilises TensorFlow for building and training machine learning models, specifically focusing on face recognition.
Integration with Industry-Standard Libraries
Technologies like Keras, OpenCV, and TensorFlow are used for training, image processing, and object detection—creating a powerful and efficient pipeline.
Utilised libraries such as Keras, OpenCV, and TensorFlow for model training, image processing, and object detection.
Face Detection Using CNN
Through Convolutional Neural Networks (CNNs), the system can detect and locate facial objects with high precision, enabling secure and real-time identification.
It can identify and locate objects, particularly faces, with the help of CNN.
Feature Extraction for Identity Recognition
The system analyzes facial features to identify specific individuals within video frames, enabling real-time person-specific recognition in dynamic environments.
Detecting facial features and identifying a particular person from the entire video frame.
Project Insight – Video Presentation
Similar Projects
Blackouts LED Light Suit
Innovative system developed for dynamic LED sequencing – a costume gadget suit for LED shows with high-performance LED displays controlled by remote.
Smart Spectacles
AI-based smart wearables using ESP32 & integrated camera modules for real-time face recognition for proactive law enforcement & public safety.
Weather Tracking Device
Weather-tracking device, used by the Government of India, collects meteorological data across multiple stations. It employs technology and sensor suite to monitor temperature, humidity, rainfall, and wind speed.
Hardware/PCB design & addon services
