Search Results for "linux performance booster"

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...

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.

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...

VGGFace2 is a large-scale face recognition dataset developed to support research on facial recognition across variations in pose, age, illumination, and identity. It consists of 3.31 million images covering 9,131 subjects, with an average of over 360 images per subject. The dataset was collected from Google Image Search, ensuring a wide diversity in ethnicity, profession, and real-world conditions. It is split into a training set with 8,631 identities and a test set with 500 identities,...

face-recognition.js is a Node.js wrapper around dlib’s face detection and recognition tools. It provides JavaScript and TypeScript APIs for loading images, locating faces, extracting face crops, and training labeled recognizers. Developers can compare unknown faces with stored examples and receive ranked predictions with distance values. The package also includes five-point and 68-point facial landmark predictors for alignment and analysis. Synchronous and Promise-based asynchronous methods...

Libface is a cross platform framework for developing face recognition algorithms and testing its performance.

Compression of face images impact the performance of face recognition (FR) systems. JPEG Region of Interest (JROI) compression maintains high image quality in facial regions while compressing the background more, with minimal impact on FR performance.