Face Recognition in Smart Watch
The Android smartwatch face recognition app with 0.91 accuracy, enabling face registration through a web portal and local recognition, providing user experience and efficient face recognition capabilities.
Face Recognition in Smart Watch
About Smart Watch
The Android smartwatch face recognition system provides offline face recognition, enabling users to register faces via a web portal and employing a locally trained model for precise identification.
The system was developed integrating with Kotlin, Python, Tensorflow, Computer Vision, AI & other ML technologies. Emphasising privacy, functionality, and future accuracy enhancements, the system delivers a resilient and dependable solution for diverse applications. The locally trained model ensures accuracy without relying on external servers, enhancing reliability and user trust.
Additionally, the system also allows for future updates and improvements, ensuring the adaptability to evolve needs and technological advancements, making it a robust and future-proof solution.
Features & Purpose Of
This Application
The Smart Watch captures facial emotions like anger, happiness, sadness, smiling and so on.
The Smart Watch captures facial emotions like anger, happiness, sadness, smiling and so on.
Computer Vision for enabling computers to gain a high-level understanding from digital images or videos.
Computer Vision for enabling computers to gain a high-level understanding from digital images or videos.
Application utilises TensorFlow for building and training machine learning models, specifically focusing on face recognition.
Application utilises TensorFlow for building and training machine learning models, specifically focusing on face recognition.
Utilised libraries such as Keras, OpenCV, and TensorFlow for model training, image processing, and object detection.
Utilised libraries such as Keras, OpenCV, and TensorFlow for model training, image processing, and object detection.
It can identify and locate objects, particularly faces, with the help of CNN.
It can identify and locate objects, particularly faces, with the help of CNN.
Detecting facial features and identifying a particular person from the entire video frame.
Detecting facial features and identifying a particular person from the entire video frame.
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