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Face recognition using FaceNet

Mtcnn face recognition, a fast c++ implementation of mtcnn

Real-time Face Recognition Using FaceNet with the

Face verification System and many more Drawbacks of Face Recognition Using FaceNet: There are some major drawback or limitations of this model. It takes 30-40 per person images with good quality of frontal face Building Face Recognition using FaceNet. Posted by Packt Publishing on July 31, 2019 at 5:30am; View Blog; Face recognition is a combination of two major operations: face detection followed by Face classification. In this tutorial, we will look into a specific use case of object detection - face recognition. The pipeline for the concerned project is as follows: Face detection: Look at an. FaceNet is the name of the facial recognition system that was proposed by Google Researchers in 2015 in the paper titled FaceNet: A Unified Embedding for Face Recognition and Clustering. It achieved state-of-the-art results in the many benchmark face recognition dataset such as Labeled Faces in the Wild (LFW) and Youtube Face Database Using FaceNet For On-Device Face Recognition With Android. Leveraging the powers of FaceNet and Firebase MLKit with Android. Shubham Panchal. Jun 21, 2020 · 4 min read. Photo by Harry Cunningham on Unsplash. The demand for face recognition systems is increasing day-by-day, as the need for recognizing, classifying many people instantly, increases. Be it your office's attendance system or a.

About FaceNet. So, the aim of the FaceNet model is to generate a 128 dimensional vector of a given face. It takes in an 160 * 160 RGB image and outputs an array with 128 elements. How is it going to help us in our face recognition project? Well, the FaceNet model generates similar face vectors for similar faces. Here, my the term similar, we mea Face recognition is a technique of identification or verification of a person using their faces through an image or a video. It captures, analyzes, and compares patterns based on the person's.. FaceNet is a face recognition system that was described by Florian Schroff, et al. at Google in their 2015 paper titled FaceNet: A Unified Embedding for Face Recognition and Clustering. It is a system that, given a picture of a face, will extract high-quality features from the face and predict a 128 element vector representation these features, called a face embedding This is a quick guide of how to get set up and running a robust real-time facial recognition system using the Pretraiend Facenet Model and MTCNN. Make a directory of your name inside the Faces folder and upload your 2-3 pictures of you. Run train_v2.py. Then run detect.py for realtime face recognization Face recognition can be done in two ways. Imagine you are building a face recognition system for an enterprise. One way of doing this is by training a neural network model (preferably a ConvNet..

Building Face Recognition using FaceNet - Data Science Centra

Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings asfeature vectors. Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet. Face recognition can be easily applied to raw images by first detecting faces using MTCNN before calculating embedding or probabilities using an Inception Resnet model. The example code at examples/infer.ipynb provides a complete example pipeline utilizing datasets, dataloaders, and optional GPU processing. Face tracking in video stream Face Recognition Framework. At Ars Futura, we developed a simple framework for creating and using a Face Recognition system. Our Face Recognition system is based on components described in this post — MTCNN for face detection, FaceNet for generating face embeddings and finally Softmax as a classifier

This article is about the comparison of two faces using Facenet python library. Human faces are a unique and beautiful art of nature. It has two eyes with eyebrows, one nose, one mouth and unique structure of face skeleton that affects the structure of cheeks, jaw, and forehead Facenet for face verification using pytorch. Pytorch implementation of the paper: FaceNet: A Unified Embedding for Face Recognition and Clustering. Training of network is done using triplet loss. This work is modified in some functionality from the original work by Taebong Moon and then retrained for the purpose of completing my BS degree Real-time face recognition program using Google's facenet.I refer to the facenet repository of davidsandberg on github.https://github.com/davidsandberg/facen.. Face Recogntion with One Shot (Siamese network) and Model based (PCA) using Pretrained Pytorch face detection and recognition models facenet-pytorch View on GitHu

Face Recognition. Simple library to recognize faces from given images. Face Recognition pipeline. Below the pipeline for face recognition: Face Detection: the MTCNN algorithm is used to do face detection; Face Alignement Align face by eyes line; Face Encoding Extract encoding from face using FaceNet; Face Classification Classify face via eculidean distrances between face encoding facenet-face-recognition. This repository contains a demonstration of face recognition using the FaceNet network ( https://arxiv.org/pdf/1503.03832.pdf) and a webcam. Our implementation feeds frames from the webcam to the network to determine whether or not the frame contains an individual we recognize

FaceNet: A Unified Embedding for Face Recognition and Clustering Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks One-shot Learning with Memory-Augmented Neural. Face recognition is currently becoming popular to be applied in various ways, especially in security systems. Various methods of face recognition have been proposed in researches and increased accuracy is the main goal in the development of face recognition methods. FaceNet is one of the new methods in face recognition technology Face Recognition using FaceNet (Survey, Performance Test, and Comparison) October 2019; DOI: 10.1109/ICIC47613.2019.8985786. Conference: 2019 Fourth International Conference on Informatics and. FaceNet is a unified system for face recognition (for both verification and identification). It is sometimes called a Siamese network . It is based on learning a Euclidean embedding per image using a deep convolutional network that encodes an image of a face into a vector..

FaceNet - Using Facial Recognition System - GeeksforGeek

A bit on FaceNet. FaceNet: A Unified Embedding for Face Recognition and Clustering; FaceNet — Using Facial Recognition System; 1. Convert the Keras model to a TFLite model. The FaceNet Keras model is available on nyoki-mtl/keras-facenet repo Last Updated on November 22, 2019 Face recognition is a computer vision Read mor Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. The FaceNet system can be used broadly thanks to multiple third-party open source implementations of. Before some months back I read a paper named as FaceNet: A Unified Embedding for Face Recognition and Clustering which present a unified system for face verification. Facenet is based on learning..

Using FaceNet For On-Device Face Recognition With Android

  1. FaceNet provides a unified embedding for face recognition, verification and clustering tasks. It maps each face image into a euclidean space such that the distances in that space correspond to.
  2. Quick Tutorial #1: Face Recognition on Static Image Using FaceNet via Tensorflow, Dlib, and Docker. This tutorial shows how to create a face recognition network using TensorFlow, Dlib, and Docker. The network uses FaceNet to map facial features as a vector (this is called embedding)
  3. Facial recognition is a biometric solution that measures unique characteristics about one's face. Applications available today include flight checkin, tagging friends and family members in photos, and tailored advertising. To perform facial recognition, you'll need a way to uniquely represent a face
  4. FaceNet is proposed by Florian Schroff in the 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering. A pretrained FaceNet model by Hiroki Taniai is used here. This project is inspired by Machine Learning Mastery. First of all, You will need to install a face detector library
  5. Katy Perry with her Face Net Python Library. Herein, deepface is a lightweight face recognition framework for Python. It currently supports the most common face recognition models including VGG-Face, Facenet and OpenFace, DeepID. It handles model building, loading pre-trained weights, finding vector embedding of faces and applying similarity metrics to recognize faces in the background. You can verify faces with a just few lines of code. It is available o

Deepface builds Facenet model, downloads it pre-trained weights, applies pre-processing stages of a face recognition pipeline (detection and alignment) in the background. You just need to call its verify or find function Despite significant recent advances in the field of face recognition [10,14,15,17], implementing face verification and recognition efficiently at scale presents serious chal-lenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distance Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors...

Face Recognition refers to identifying a face in a given image and verifying the person in the image. They are used in a wide range of applications, including but not limited to: User Verification, Attendance Systems, Robotics and Augmented Reality. With the growth in applications, we are likely to see great development in the field This is a TensorFlow implementation of the face recognizer described in the paper FaceNet: A Unified Embedding for Face Recognition and Clustering. The project also uses ideas from the paper Deep Face Recognition from the Visual Geometry Group at Oxford. The code is tested using Tensorflow r1.7 under Ubuntu 14.04 with Python 2.7 and Python. Human FaceNet: Human Identification using Face Recognition Technology by Samah A. F. Manssor et al. Permission to make digital or hard copies of part or all of this work for personal or classroom us Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors. Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use. FaceNet is a face recognition system that was described by Florian Schroff, et al. at Google in their 2015 paper titled FaceNet: A Unified Embedding for Face Recognition and Clustering. Face.

GitHub - TheSUPERCD/FacenetFaceID: This is a "Face

Face Recognition using FaceNet and Firebase MLKit on Androi

  1. d that the dlib face recognition post relied on two important external libraries
  2. FaceNet is a Deep Neural Network used for face verification, recognition and clustering. It directly learns mappings from face images to a compact Euclidean plane. When an input image of 96*96 RGB is given it simply outputs a 128-dimensional vector which is the embedding of the image
  3. sults of face detection, face recognition is performed using FaceNet. FaceNet directly learns a mapping from face images to a compact Euclidean space where distances directly corre-spond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standar
  4. Face Recognition Based on Facenet Built using Facenet 's state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.2% on the Labeled Faces in the Wild benchmark
  5. #2 best model for Face Verification on CK+ (Accuracy metric
  6. g Interfaces 124. Applications 192. Artificial Intelligence 78. Blockchain 73. Build Tools 113. Cloud Computing 80. Code Quality 28. Collaboration 32. Command Line Interface 49.

hie, i am using NCS2 stick with latest openvino toolkit (l_openvino_toolkit_p_2019.1.144) in ubuntu 16.04 environment. Can we implement face recognition using NCS2,opencv,tensorflow. ive gone through so many links where only face detection was implemented.i need face recognition ,is there any source that i can go through Face Recognition using Tensorflow This is a TensorFlow implementation of the face recognizer described in the paper FaceNet: A Unified Embedding for Face Recognition and Clustering. The project also uses ideas from the paper Deep Face Recognition from the Visual Geometry Group at Oxford

How to create a Face Recognition Model using FaceNet Keras

Face Recognition using Tensorflow . This is a TensorFlow implementation of the face recognizer described in the paper FaceNet: A Unified Embedding for Face Recognition and Clustering.The project also uses ideas from the paper Deep Face Recognition from the Visual Geometry Group at Oxford.. Compatibilit Face recognition attendance system 1. Face Recognition Attendance System Jigar Patel (44) Amey Mohite (61) Naomi Kulkarni (65) Shivani Sharma (93) PROJECT: DUMMY Group No: 8 2. Agenda • Definition • Scope • Project Status • Methodology • Data Flow Diagrams • System Architecture • Database Dictionary • Database Diagram • Demo • Designing of System (Snapshots) • Learning. I want to create a face recognition with facenet but most website that I have referred they used tensorflow version 1 instead version 2. I have changed the program a little bit so that it can run in.

Face recognition with Google's FaceNet deep neural network & TensorFlow. Project details. Project links. Homepage Statistics. GitHub statistics: Stars: Forks: Open issues/PRs: View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery. Meta. License: MIT. Maintainer: Jonathan Lancar. Maintainers jonaphin Release history Release notifications | RSS. hello everyone i want to make a program for detect my face. :) using web cam and vb software. so i dont know how to make this program. please help me. thank you!!!! hasala Friday, September 2, 2016 4:43 A Facial recognition is concerned with identifying who is the person in the image by detecting the face while facial verification determines if the given two images are of the same person or not. Here, to build our system we will require a model that can detect faces from the images and convert the facial details into a 128-dimensional vector which we can use later for facial recognition. Face and Landmark Detection using mtCNN ()Google FaceNet. Google's FaceNet is a deep convolutional network embeds people's faces from a 160x160 RGB-image into a 128-dimensional latent space and allows feature matching of the embedded faces. By saving embeddings of people's faces in a database you can perform feature matching which allows to recognize a face since the euclidean distance. multi-face recognition by using the combination of FaceNet and Support Vector Machine (SVM). In this proposed system, feature extraction is done by using FaceNet by embedding 128 dimensions per face and SVM is used to classify the given training data with the extracted feature of FaceNet. In this system for face recognition, we have to do three steps: pre-processing, feature extraction and.

GitHub - lincolnhard/facenet-darknet-inference: Face

I am using Facenet for face recognition. Ask Question Asked 4 months ago. Active 20 days ago. Viewed 60 times 0. 1. In this code it picks a random image from my test folder and predicts the output by giving name of the person matching with accuracy score, but instead of randomly picking image, I want to give my own image path for prediction , please help me out. The below code takes all images. Face Recognition based Surveillance System Using FaceNet and MTCNN on Jetson TX2 Edwin Jose Department of Electronics Cochin University of Science and Technology Kochi, India edwin.jose@cusat.ac.

Face Recognition with MTCNN and FaceNet:-First Amit Kumar presented a detailed overview of Face Recognition with MTCNN and FaceNet. Face Recognition involves a pipeline of Face Detection, Feature Extraction and Face Classification. MTCNN is a face detector and has a series of three networks :- P-net: Proposal Network to propose candidate facial regions; R-net: Refine Network to filter and. Probably, because the public model of facenet is not well trained. Then I also tried with AWS rekognition API but it takes around 1 sec to get a response from the server, so I can't use it for real-time face recognition. Is there any model/API, which is enough accurate and fast for real-time face recognition? Thanks In this tutorial, we will learn Face Recognition from video in Python using OpenCV. So How can we Recognize the face from video in Python using OpenCV we will learn in this Tutorial. Now let's begin. We will divide this tutorial into 4 parts. So you can easily understand this step by step. We detect the face in any Image. We detect the face in image with a person's name tag. Detect the.

GitHub - R4j4n/Face-recognition-Using-Facenet-On

Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity Facial recognition is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces. The state of the art tables for this task are contained mainly in the consistent parts of the task : the. Hello friends... Today we are going to show you application of Facnet model for face recognition in image and video in real time. Here we will train model wi.. Basic face application using pre-trained deep learning model.To better understand the face recognition using deep learning, you can read my Medium article at..

For instance, Google declared that face alignment increases its face recognition model FaceNet from 98.87% to 99.63%. This is almost 1% accuracy improvement which means a lot for engineering studies. Here, you can find a detailed tutorial for face alignment in Python within OpenCV. Face alignment Conclusion. OpenFace is a lightweight face recognition model. It is not the best but it is a. For face recognition, we will crop faces using the face location obtained through haar cascade detector from images and resize into 96x96x3 dimensions and use FaceNet Deep Learning Model which has been already trained on millions of images using the triplet loss function as defined above. FaceNet inputs a face image (or batch of m face images) as a tensor of shape (m,nC,nH,nW)=(m,3,96,96) and.

I want to know how to integrate this using Deepstream. There is also a face detection model that I found facenet-120. I am also willing to use this model instead of using mtcnn solution for detecting a face. Also suggest how to use this with the facenet model. My goal is to output 128D vector for one face from the facenet model. Thanks! In this paper, a novel 3D face reconstruction technique is proposed along with a sequential deep learning-based framework for face recognition. It uses the voxels generated from the voxelization process. It uses the reflection principle for generating the reconstructed point in 3D using the mid-face plane. From the reconstructed face, a sequential deep learning framework is developed to. Face recognition performance is evaluated on a small subset of the LFW dataset which you can replace with your own custom dataset e.g. with images of your family and friends if you want to further experiment with the notebook. After an overview of the CNN architecure and how the model can be trained, it is demonstrated how to: Detect, transform, and crop faces on input images. This ensures.

Making your own Face Recognition System in Python - John

Building a real time Face Recognition system using pre

Building Face Recognition Using FaceNet. In the previous chapter, we learned how to detect objects in an image. In this chapter, we will look into a specific use case of object detection—face recognition. Face recognition is a combination of two major operations: face detection, followed by face classification. The (hypothetical) client that provides our business use case for us in this. Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace. FaceNet is the name of the facial recognition system that was proposed by Google Researchers in 2015 in the paper titled FaceNet: A Unified Embedding Read More » The post FaceNet - Using Facial Recognition System appeared first on GeeksforGeeks. from GeeksforGeeks https://ift.tt/2YtU0Wb via IFTT FaceNet was published in a paper entitled FaceNet: A Unified Embedding for Face Recognition and Clustering at CVPR 2015 (a world-class conference for computer vision). When it was released it smashed the records of two top facial recognition academic datasets (Labeled Faces in the Wild and YouTube Faces DB) by a whopping 30% (on both.

OpenFace Open Source Real Time Facial Recognition Software

GitHub - davidsandberg/facenet: Face recognition using

Face recognition implementation using FACENET tensorflow. Hello I want a production ready to use application for real time facial recognition using. I prefer facenet [ to view URL] Skills: Artificial Intelligence. See more: face recognition video using java, face recognition project using webcam, face recognition android using opencv, openface tensorflow, facenet tutorial, how to use. FaceNet is a system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification, and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors

Video: Building Face Recognition using FaceNet - Mobile

For now I'm using facenet embedding as input with a SVM classifier. With the limited number of faces that I have to distinguish, this gives really good results. However I'm not quite sure what to do in order to detect an unknown face as such. I tried training the classier on family faces as well as on a hundred more faces from the LFW database. This article demonstrates a very effective approach for face recognition when the dataset is very limited. Using only one image per person (one-shot learning), we managed to create a highly accurate model for recognizing company employees in real-time. Convolutional Neural Networks (CNNs) have taken the computer vision community by storm, significantly surpassing the state-of-the-art. For Face recognition, image processing techniques along with machine learning algorithms are required and used for face detection and face identification. The proposed method is done by initially face encoding the input image using the Histogram Object Gradient (HOG) method and the face detection is done through the facenet algorithm, which consists of several library functions in the Dlib. Then facial features extraction is performed using the Google FaceNet embedding model. And finally, the classification task has been performed by Support Vector Machine (SVM). Experiments signify that this mentioned approach gives a remarkable performance on masked face recognition. Besides, its performance has been also evaluated within excessive facial masks and found attractive outcomes. Webcam Face Tracking and Face Recognition. As promised, we will now have a look at how to implement face tracking and face recognition using your webcam. In this example I am gonna use my webcam to track and recognize faces of some Big Bang Theory Protagonists again, but of course you can use this bit of code for tracking and recognizing.

by Sigurður Skúli. Making your own Face Recognition System. Face recognition is the latest trend when it comes to user authentication. Apple recently launched their new iPhone X which uses Face ID to authenticate users. OnePlus 5 is getting the Face Unlock feature from theOnePlus 5T soon. And Baidu is using face recognition instead of ID cards to allow their employees to enter their offices In this paper, the authors present a face recognition system called FaceNet. This system uses a deep convolutional neural network which optimizes the embedding, rather than using an intermediate bottleneck layer. The authors state that the most important aspect of this method is the end-to-end learning of the system. The team trained the convolutional neural network on a CPU cluster for 1,000. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors. Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use. I did this by repeatedly training a face recognition model and then using graph clustering methods and a lot of manual review to clean up the dataset. In the end, about half the images are from VGG and face scrub. Also, the total number of individual identities in the dataset is 7485. I made sure to avoid overlap with identities in LFW so the LFW evaluation would be valid. The network training. Face Recognition using Python. In this section, we shall implement face recognition using OpenCV and Python. First, let us see the libraries we will need and how to install them: OpenCV; dlib; Face_recognition; OpenCV is an image and video processing library and is used for image and video analysis, like facial detection, license plate reading, photo editing, advanced robotic vision, optical.

Deep Learning for Computer Vision (2/4): Object Analytics

Face detection and recognition using FaceNet, MTCNN and

Raspberry Pi Face Recognition. This post assumes you have read through last week's post on face recognition with OpenCV — if you have not read it, go back to the post and read it before proceeding.. In the first part of today's blog post, we are going to discuss considerations you should think through when computing facial embeddings on your training set of images FaceNet by google; dlib_face_recognition_resnet_model_v1 by face_recognition; It looks both working fine.. but in real-time implementation, Is there some thing important to understand the performance ? Or any comparison checks done on real time / large data sets ? Thanks in advance. deep-learning cnn image-recognition  Share. Improve this question. Follow edited Jun 16 '20 at 11:08.

facenet: Face recognition using Tensorflo

Facenet: Using Ensembles of Face Embedding Sets. Ask Question Asked 3 years, 1 month ago. Active 3 years ago. Viewed 462 times 2. 1. The Facenet is a deep learning model for facial recognition. It is trained for extracting features, that is to represent the image by a fixed length vector called embedding. After training, for each given image, we take the output of the second last layer as its. face recognition asstill pose,remains challenging task due to various factors such ex- pression, illumination, partial occlusion, etc. In particular, the most significant appearance variations are stemmed from poses which leads to severe performance degeneration. In this paper, we propose a novel Deformable Face Net (DFN) to handle the pose variations for face recognition. The deformable. ML | Face Recognition Using Eigenfaces (PCA Algorithm) 23, Mar 20. Face recognition using Artificial Intelligence. 25, Nov 20. Deep Face Recognition. 03, May 20. Google Chrome Dino Bot using Image Recognition | Python . 27, Sep 19. Python | Named Entity Recognition (NER) using spaCy. 17, Jun 19. Python | Reading contents of PDF using OCR (Optical Character Recognition) 16, Jan 19. How to. To learn how to create your own face recognition dataset, just keep reading! Looking for the source code to this post? Jump Right To The Downloads Section . How to create a custom face recognition dataset. In this tutorial, we are going to review three methods to create your own custom dataset for facial recognition. The first method will use OpenCV and a webcam to (1) detect faces in a video. Google's Near Perfect Face Recognition System - FaceNet. March 19, 2015 Edmondo Burr News, Technology 2. google_facial recognition. Twitter Facebook LinkedIn Email Reddit. Google's FaceNet can detect a face in a 260 million image database with almost 100% accuracy. In a wild database sample called 'Labeled Faces in the Wild', taken from all over the internet- a sample of 13,000 faces.

Schroff facenet a unified embedding for face recognitionFace Recognition: Real-Time Face Recognition System using

To handle these constraints effectively, we propose a hybrid domain based face recognition using Asymmetric Region Local Binary Pattern (ARLBP) and Histogram oriented gradient (HOG) techniques. The preprocessing has been carried out on all the images to extract the face region by removing the background and resizing to 100x100. The face features are extracted using ARLBP and HoG techniques for. Coding Face Recognition using Python and OpenCV. We are going to divide the Face Recognition process in this tutorial into three steps: Prepare Training Data: Read training images for each person/subject along with their labels, detect faces from each image and assign each detected face an integer label of the person it belongs. Train Face Recognizer: Train OpenCV's LBPH recognizer by feeding. One-shot learning is a classification task where one, or a few, examples are used to classify many new examples in the future. This characterizes tasks seen in the field of face recognition, such as face identification and face verification, where people must be classified correctly with different facial expressions, lighting conditions, accessories, and hairstyles given one or a few template.

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