Open Source ChromeOS Facial Recognition Software

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, making it suitable for benchmarking and large-scale model training. Alongside the dataset, the repository provides pre-trained models based on ResNet-50 and SE-ResNet-50 architectures, trained with both MS-Celeb-1M pretraining and fine-tuning on VGGFace2. These models achieve strong verification performance on benchmarks such as IJB-B and include variants with lower-dimensional embeddings for compact feature representation. The project also includes preprocessing tools, face detection scripts, and etc.

With the growing need to exchange information and share resources, information security has become more important than ever in both the public and private sectors. Although many technologies have been developed to control access to files or resources, to enforce security policies, and to audit network usages, there does not exist a technology that can verify that the user who is using the system is the same person who logged in. FaceAccess provides a prototype implementation as a "login module" of an information system. The goal is to enhance the level of system security by periodically checking the user’s identity without disrupting the user’s activities. Installation instructions can be found in the package. If you need anymore guidance, please use the Wiki to post any kind of inquiry. NB: Please Donate to support the development of this project. PM me for other means. Any kind of support will be very much appreciated. Thanks a bunch.

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 to work in real-world applications like mobile apps, robotics, and other resource-constrained environments.

System using to verify personality by face's photo.

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.

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.

Artificial neural network with eigenfaces for face recognition

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

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