Face-Recognition-based-Attendance-System - An implementation of Attendance System in python.

Related tags

Deep LearningFace-Recognition-based-Attendance-System
Overview

Face-Recognition-based-Attendance-System

A real time implementation of Attendance System in python.

Pre-requisites

To understand the implentation of Facial recognition based Attendance System you must have,
– Basic understanding of Image Classification
– Knowledge of Python and Deep Learning

Dependencies

1- OpenCV
2- dlib
3- face_recognition
4- os
5- imutils
6- numpy
7- pickle
8- datetime
9- Pandas

Note: To install dlib and face_recognition, you need to create a virtual environment in your IDE first.

Overview

Face is the crucial part of the human body that uniquely identifies a person. Using the face characteristics the face recognition projects can be implemented. The technique which I have used to implent this project is Deep Metric Learning.

What is Deep Metric Learning ?

If you have any prior experience with deep learning you know that we typically train a network to:
Accept a single input image
And output a classification/label for that image
However, deep metric learning is different. Instead, of trying to output a single label, we are outputting a real-valued feature vector. This technique can be divided into three steps,

Face Detection

The first task that we perform is detecting faces in the image(photograph) or video stream. Now we know that the exact coordinates or location of the face, so we extract this face for further processing.

Feature Extraction

Now see we have cropped out the face from the image, so we extract specific features from it. Here we are going to see how to use face embeddings to extract these features of the face. Here a neural network takes an image of the face of the person as input and outputs a vector that represents the most important features of a face. For the dlib facial recognition network which I have used here, the output feature vector is 128-d (i.e., a list of 128 real-valued numbers) that is used to quantify the face. This output feature vector is also called face embeddings.

Comparing Faces

We have face embeddings for each face in our data saved in a file, the next step is to recognize a new image that is not in our data. Hence the first step is to compute the face embedding for the image using the same network we used earlier and then compare this embedding with the rest of the embeddings that we have. We recognize the face if the generated embedding is closer or similar to any other embedding.

What's include in this repository ?

Three files only. These are
1- Feature_extractor.py for extracting and saving the features from images (128-d vector for each face) of persons provided
2- attendace.py for real time implementation of Face-Recognition-based-Attendance-System.
3- README.md which you are reading.

How to implement ?

Create a folder named 'images' at the same location where you have kept the python files mentioned above. In this folder you will create sub folders, each having the the images of the of a persons whom you want the program to recognize. Each folder should have 3-4 images. Change the names of subfolder to the names of the people to be identified. Now first run Feature_extractor.py. This will takes some time and provide you a file named face_enc containing the features extracted from the the images. This file will be used by attendace.py. Now run attendace.py to real time implementation of Face-Recognition-based-Attendance-System. If a person recognized by the program then his/her name and time of recognization will be stored in record.csv . You don't need this file, program will create this file itself and will keep maintaining the attendance data in it.
To further understand the working of program, just go through the code files and read the comments in it.
Owner
Muhammad Zain Ul Haque
Muhammad Zain Ul Haque
GitHub Repository
Reinfore learning tool box, contains trpo, a3c algorithm for continous action space

RL_toolbox all the algorithm is running on pycharm IDE, or the package loss error may exist. implemented algorithm: trpo a3c a3c:for continous action

yupei.wu 44 Oct 10, 2022
Code release of paper "Deep Multi-View Stereo gone wild"

Deep MVS gone wild Pytorch implementation of "Deep MVS gone wild" (Paper | website) This repository provides the code to reproduce the experiments of

François Darmon 53 Dec 24, 2022
This is the repository for paper NEEDLE: Towards Non-invertible Backdoor Attack to Deep Learning Models.

This is the repository for paper NEEDLE: Towards Non-invertible Backdoor Attack to Deep Learning Models.

1 Oct 25, 2021
Yolo object detection - Yolo object detection with python

How to run download required files make build_image make download Docker versio

3 Jan 26, 2022
The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.

This repository is the official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness. Requirements pip install -r requi

Jie Ren 17 Dec 12, 2022
An implementation of the methods presented in Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

An implementation of the methods presented in Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

Andrew Jesson 9 Apr 04, 2022
这是一个利用facenet和retinaface实现人脸识别的库，可以进行在线的人脸识别。

Facenet+Retinaface：人脸识别模型在Pytorch当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络，分别是retinaface和

Bubbliiiing 102 Dec 30, 2022
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning

H-Transformer-1D Implementation of H-Transformer-1D, Transformer using hierarchical Attention for sequence learning with subquadratic costs. For now,

Phil Wang 123 Nov 17, 2022
【Arxiv】Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution

SANet Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution Dependencies numpy==1.18.5 scikit_image==0.16.2 torchvision==0.8.1 to

36 Jan 05, 2023
Very large and sparse networks appear often in the wild and present unique algorithmic opportunities and challenges for the practitioner

Sparse network learning with snlpy Very large and sparse networks appear often in the wild and present unique algorithmic opportunities and challenges

Andrew Stolman 1 Apr 30, 2021
The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue. How do I cite D-REX? For now, cite

Alon Albalak 6 Mar 31, 2022
A simple implementation of Kalman filter in Multi Object Tracking

kalman Filter in Multi-object Tracking A simple implementation of Kalman filter in Multi Object Tracking 本实现是在https://github.com/liuchangji/kalman-fil

124 Dec 29, 2022
Head and Neck Tumour Segmentation and Prediction of Patient Survival Project

Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival Welcome to the Head and Neck Tumour Segmentation and Prediction of Patient Surviv

5 Oct 20, 2022
"MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction" (CVPRW 2022) & (Winner of NTIRE 2022 Challenge on Spectral Reconstruction from RGB)

MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (CVPRW 2022) Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Z

Yuanhao Cai 274 Jan 05, 2023
This code is an unofficial implementation of HiFiSinger.

HiFiSinger This code is an unofficial implementation of HiFiSinger. The algorithm is based on the following papers: Chen, J., Tan, X., Luan, J., Qin,

Heejo You 87 Dec 23, 2022
ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)

ICON: Implicit Clothed humans Obtained from Normals Yuliang Xiu · Jinlong Yang · Dimitrios Tzionas · Michael J. Black CVPR 2022 News 🚩 [2022/04/26] H

Yuliang Xiu 1.1k Jan 04, 2023
Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer) Introduction By applying the

Son Gyo Jung 1 Jul 09, 2022
Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion

Feature-Style Encoder for Style-Based GAN Inversion Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion. Code will

InterDigital 63 Jan 03, 2023
source code and pre-trained/fine-tuned checkpoint for NAACL 2021 paper LightningDOT

LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval This repository contains source code and pre-trained/fine-tun

Siqi 65 Dec 26, 2022
Machine learning library for fast and efficient Gaussian mixture models

This repository contains code which implements the Stochastic Gaussian Mixture Model (S-GMM) for event-based datasets Dependencies CMake Premake4 Blaz

Omar Oubari 1 Dec 19, 2022