Pretrained Pytorch face detection (MTCNN) and recognition (InceptionResnet) models

A collection of face related papers

Sample projects for Computer Vision with Raspberry Pi and Movidius Neural Compute Stick

Face identification with cnn+triplet-loss written by Keras.

얼굴 인식에 대한 기술 동향 및 관련 모델 자료

FRI (Face Recognition and Identification)

Open DRUWA - Open Deep Realtime User Welcoming Assistant

Proiect licenta

BWS HTML5 Unified User Interface

Detect, recognize and verify faces using hybrid features: “deep” features from VGG-net + HoG + LBP. Hybrid Features help increase recognition significantly

The purpose of this Android app is to use Kairos's SDK for Android in order to implement facial recognition. Features of this app include: registering users with an image and name and identifying users when given an image.

中科院软件所XLab的机器人UI交互模块

It is an Face Identification based automatic attendance management system which will be useful taking attendance during online lecture.

Identify unknown persons that are not registered and stores their images with current date and time.

Face recognition keras model based on yolov3.

Application for finding the most similar person to you among known celebrities, politicians etc.

Deep face identification in Keras

Facial Keypoint Detection and Real-time Filtering

Real time face Identification using opencv on python (Opencv kullanarak Python da gerçek zamanlı yüz tanıma (kimlik tespiti ) uygulaması)

Step 1 : Register your Face : A. Take a video of few seconds of your face. B. Create samples of images from Video to train Convolutional Neural Network. C. Train the model with those images (class = 'YES') and some images that is not your face (class = 'NO') D. Convert the model output (.h5) into .mlmodel E. import it into your Xcode for Production. Step 2 : Next Time while log in A. Take image from Camera B. Give the image to CNN for calssification. C. If it's you then "Successful Log in" D. Other-wise show some pop-up like "It's not you"

In this thesis, we study two problems based on clustering algorithms. In the first problem, we study the role of visual attributes using an agglomerative clustering algorithm to whittle down the search area where the number of classes is high to improve the performance of clustering. We observe that as we add more attributes, the clustering performance increases overall. In the second problem, we study the role of clustering in aggregating templates in a 1:N open set protocol using multi-shot video as a probe. We observe that by increasing the number of clusters, the performance increases with respect to the baseline and reaches a peak, after which increasing the number of clusters causes the performance to degrade. Experiments are conducted using recently introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition datasets.

gui implementation

Signin with your face working as the security system.

An End to End Real Time Face Identification and attendance system using CNN

Machine Learning model which uses kNN Algorithm & Computer Vision to recognise different faces

Recognizes faces via online or offline recignizer. Powered by OpenCV.

One-shot face identification