Search Results for "open source"

The MediaPipe Face Detection model is a high-performance, real-time face detection solution that uses machine learning to identify faces in images and video streams. It is optimized for mobile and embedded platforms, offering fast and accurate face detection while maintaining a small memory footprint. This model supports multiple face detections and is highly efficient, making it suitable for a variety of applications such as augmented reality, user authentication, and facial expression analysis.

Blazeface is a lightweight, high-performance face detection model designed for mobile and embedded devices, developed by TensorFlow. It is optimized for real-time face detection tasks and runs efficiently on mobile CPUs, ensuring minimal latency and power consumption. Blazeface is based on a fast architecture and uses deep learning techniques to detect faces with high accuracy, even in challenging conditions. It supports multiple face detection in varying lighting and poses, and is designed...

face-api.js is a JavaScript and TypeScript library for face analysis in web browsers and Node.js. It runs neural networks through TensorFlow.js and accepts images, videos, canvases, or tensors as input. The API can locate one or many faces using several detector models with configurable accuracy and performance settings. It can identify facial landmarks, compute recognition descriptors, classify expressions, and estimate age and gender. High-level chained methods let developers combine...

1>Face Recognition on real-time based. 2>shirt -color recognition on image based.

Artificial neural network with eigenfaces for face recognition

System using to verify personality by face's photo.

Java framework for face detection and face recognition based on different plugin and filter types. Includes Eigenfaces in pure Java, OpenCV detection via JNI, integration of the Betaface.com Web Service, skin color filter, Adobe XMP Export and a nice GUI

WHAY is a Video-based Face Recognition tool written in MATLAB. It aims to exploits PCA recognizing as better as possible and tests the limits of this approach.