Search Results for "set"

This is a free and open-source liveliness check and face recognition project. Essentially, it is an application that can be used as a standalone server or deployed in the cloud. You don’t need prior machine learning skills to set up and use. The application is customizable react based mobile friendly UI and Python based backend. The program is a real-time face detection application. It allows you to detect faces using your webcam and displays the video feed with oval drawn around the detected faces. When you run the program, a GUI window will appear. The window appears to do liveliness check and face detection. ...

Exadel CompreFace is a free and open-source face recognition GitHub project. Essentially, it is a docker-based application that can be used as a standalone server or deployed in the cloud. You don’t need prior machine learning skills to set up and use CompreFace. The system provides REST API for face recognition, face verification, face detection, face mask detection, landmark detection, age, and gender recognition. The solution also features a role management system that allows you to easily control who has access to your Face Recognition Services. CompreFace is delivered as a docker-compose config and supports different models that work on CPU and GPU. ...

...A key part to many of these applications are visual recognition tasks such as image classification, object detection and image similarity. This repository provides examples and best practice guidelines for building computer vision systems. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in Computer Vision algorithms, neural architectures, and operationalizing such systems. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utility around loading image data, optimizing and evaluating models, and scaling up to the cloud.

...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. ...

This code uses a technique originally developed for facial recognition to describe shear stress distributions in open channel flow. In this approach, a synthetic database of images representing normalized shear stress distributions is formed from the training data set using recurrence plot analysis. A face recognition algorithm is then employed to synthesize the recurrence plots and transform the original database into short-dimension vectors containing similarity weights proportional to the principal components of the distribution of images. These vectors capture the intrinsic properties of the boundary shear stress distribution of the cases in the training set, and are sensitive to variations of the corresponding hydraulic parameters. ...

Research on automatic face recognition in images has rapidly developed into several inter-related lines, and this research has both lead to and been driven by a disparate and expanding set of commercial applications. The large number of research activities is evident in the growing number of scientific communications published on subjects related to face processing and recognition. Index Terms: face, recognition, eigenfaces, eigenvalues, eigenvectors, Karhunen-Loeve algorithm.

...In face detection, one does not have this additional information. Face detection is mostly used along with facial recognition feature to extract faces out of an image or video feed and identify the faces against a set of stored images. This feature can be used for tracking prisoners, attendance and signing in to user accounts. The Viola - Jones Algorithm presents a novel face detection technique, we would implement this technique for face detection and then use eigen faces method to recognize the person in the image. Once recognized the person can easily sign in to any applications without the need of typing passwords or usernames.

This project aims at developing a face authentication system, using the Eigenfaces, and Eigenfeatures. The Approach is a principal component analysis method, in which a set of characteristic pictures are used to describe the variation between face images.