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
Classification Modeling: Probability of Default

Credit Risk Modeling in Python Introduction: If you've ever applied for a credit card or loan, you know that financial firms process your information

Aktham Momani 2 Nov 07, 2022
Joint-task Self-supervised Learning for Temporal Correspondence (NeurIPS 2019)

Joint-task Self-supervised Learning for Temporal Correspondence Project | Paper Overview Joint-task Self-supervised Learning for Temporal Corresponden

Sifei Liu 167 Dec 14, 2022
This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.

Official Pytorch Implementation for GLFC [CVPR-2022] Federated Class-Incremental Learning This is the official implementation code of our paper "Feder

Race Wang 57 Dec 27, 2022
🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

🆕 Are you looking for a new YOLOv3 implemented by TF2.0 ? If you hate the fucking tensorflow1.x very much, no worries! I have implemented a new YOLOv

3.6k Dec 26, 2022
Constructing interpretable quadratic accuracy predictors to serve as an objective function for an IQCQP problem that represents NAS under latency constraints and solve it with efficient algorithms.

IQNAS: Interpretable Integer Quadratic programming Neural Architecture Search Realistic use of neural networks often requires adhering to multiple con

0 Oct 24, 2021
A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation.

TiSASRec.paddle A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation. Introduction 论文：Time Interval Aware Sel

Paddorch 2 Nov 28, 2021
State-of-the-art language models can match human performance on many tasks

Status: Archive (code is provided as-is, no updates expected) Grade School Math [Blog Post] [Paper] State-of-the-art language models can match human p

OpenAI 259 Jan 08, 2023
FairyTailor: Multimodal Generative Framework for Storytelling

FairyTailor: Multimodal Generative Framework for Storytelling

Eden Bens 172 Dec 30, 2022
PyTorch implementation of "PatchGame: Learning to Signal Mid-level Patches in Referential Games" to appear in NeurIPS 2021

PatchGame: Learning to Signal Mid-level Patches in Referential Games This repository is the official implementation of the paper - "PatchGame: Learnin

Kamal Gupta 22 Mar 16, 2022
Subnet Replacement Attack: Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks

Subnet Replacement Attack: Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks Official implementation of paper Towards Practic

Xiangyu Qi 8 Dec 30, 2022
A repo for Causal Imitation Learning under Temporally Correlated Noise

CausIL A repo for Causal Imitation Learning under Temporally Correlated Noise. Running Experiments To re-train an expert, run: python experts/train_ex

Gokul Swamy 5 Nov 01, 2022
Package to compute Mauve, a similarity score between neural text and human text. Install with `pip install mauve-text`.

MAUVE MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE

Krishna Pillutla 182 Jan 02, 2023
Pansharpening by convolutional neural networks in the full resolution framework

Z-PNN: Zoom Pansharpening Neural Network Pansharpening by convolutional neural networks in the full resolution framework is a deep learning method for

20 Nov 24, 2022
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation.

Training Script for Reuse-VOS This code implementation of CVPR 2021 paper : Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Vi

HYOJINPARK 22 Jan 01, 2023
Code for ICMI2020 and ICMI2021 papers: "Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle" and "ML-PersRef: A Machine Learning-based Personalized Multimodal Fusion Approach for Referencing Outside Objects From a Moving Vehicle"

ML-PersRef This repository has python code (in jupyter notebooks) for both of the following papers: ML-PersRef: A Machine Learning-based Personalized

3 Sep 04, 2022
EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation.

This repository contains data and code for our EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation. Please contact me at

9 Oct 28, 2022
How Do Adam and Training Strategies Help BNNs Optimization? In ICML 2021.

AdamBNN This is the pytorch implementation of our paper "How Do Adam and Training Strategies Help BNNs Optimization?", published in ICML 2021. In this

Zechun Liu 47 Sep 20, 2022
Mini-hmc-jax - A simple implementation of Hamiltonian Monte Carlo in JAX

mini-hmc-jax This is a simple implementation of Hamiltonian Monte Carlo in JAX t

Martin Marek 6 Mar 03, 2022
Awesome Monocular 3D detection

Awesome Monocular 3D detection Paper list of 3D detetction, keep updating! Contents Paper List 2022 2021 2020 2019 2018 2017 2016 KITTI Results Paper

Zhikang Zou 184 Jan 04, 2023
Open-source implementation of Google Vizier for hyper parameters tuning

Advisor Introduction Advisor is the hyper parameters tuning system for black box optimization. It is the open-source implementation of Google Vizier w

tobe 1.5k Jan 04, 2023