Realtime Face Anti Spoofing with Face Detector based on Deep Learning using Tensorflow/Keras and OpenCV

Overview

Realtime Face Anti-Spoofing Detection 🤖

Realtime Face Anti Spoofing Detection with Face Detector to detect real and fake faces

Python contributions welcome Forks Stargazers

Please star this repo if it is useful for you! 🌟



Changelog

All notable changes to this project will be documented in this file. The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[1.1] - 10/09/2021

Added

  • Added realtime bluriness detector based on OpenCV

[1.0] - 03/09/2021

Added

  • First commit with Face Detector, updated README
  • Fixed minor issues with models not loading

Why Build This? 🤔

Face anti-spoofing systems has lately attracted increasing attention due to its important role in securing face recognition systems from fraudulent attacks. This project aims to provide a starting point in recognising real and fake faces based on a model that is trained with publicly available dataset

Where to use? 🔨

This Face Anti Spoofing detector can be used in many different systems that needs realtime facial recognition with facial landmarks. Potentially could be used in security systems, biometrics, attendence systems and etc.

Can be integrated with hardware systems for application in offices, schools, and public places for various use cases.

Datasets and Library 📗

The model is trained using Tensorflow from publicly available datasets. Below listed are the data sources that the model is trained on:

CASIA: https://github.com/namtpham/casia2groundtruth

OULU: https://sites.google.com/site/oulunpudatabase/

NUAA: http://parnec.nuaa.edu.cn/_upload/tpl/02/db/731/template731/pages/xtan/NUAAImposterDB_download.html

3DDFA: https://github.com/cleardusk/3DDFA (Face Detector Library)

Please obtain the necessary permissions before using the datasets as above.

Prerequisites

All the required libraries are included in the file requirements.txt. Tested on Ubuntu 20.04 with Python3.8. Face Detector library, 3DDFA aka (face_det) is added as part of the repo for easy development.

Installation 💻

  1. Clone the repo
$ git clone https://github.com/Prem95/face-liveness-detector.git
  1. Change your directory to the cloned repo
$ cd face-liveness-detector
  1. Run the following command in your terminal
$ pip install -r requirements.txt
  1. Build the Face Detector library
$ cd face_det
$ sh build.sh

Usage

Run the following command in your terminal

$ python3 main.py

Note: Current Face Anti Spoofing threshold is set at a value of 0.70. This can be finetuned based on different situations as needed.

Contribution

Feel free to file a new issue with a respective title and description on the the face-liveness-detector repository.

Feature Request

Please also submit a pull request for any issues that might appear or any enhancements/features that could make this project perform better. I would love to review your pull request!

Code of Conduct 👍

You can find our Code of Conduct here.

License 👍

All rights reserved according to MIT © Prem Kumar

Owner
Prem Kumar
Machine Learning Engineer focused on Face Recognition.
Prem Kumar
Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore

[AI6122] Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6122 of Semester 1, AY2021-2022, starting from 08/2021. The instructor of this course

HT. Li 5 Sep 12, 2022
Codebase for testing whether hidden states of neural networks encode discrete structures.

structural-probes Codebase for testing whether hidden states of neural networks encode discrete structures. Based on the paper A Structural Probe for

John Hewitt 349 Dec 17, 2022
Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE)

OG-SPACE Introduction Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE) is a computational framewo

Data and Computational Biology Group UNIMIB (was BI*oinformatics MI*lan B*icocca) 0 Nov 17, 2021
Implementation of " SESS: Self-Ensembling Semi-Supervised 3D Object Detection" (CVPR2020 Oral)

SESS: Self-Ensembling Semi-Supervised 3D Object Detection Created by Na Zhao from National University of Singapore Introduction This repository contai

125 Dec 23, 2022
Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

Rainbow 🌈 An implementation of Rainbow DQN which reaches a median HNS of 205.7 after only 10M frames (the original Rainbow from Hessel et al. 2017 re

Dominik Schmidt 31 Dec 21, 2022
Simple reimplemetation experiments about FcaNet

FcaNet-CIFAR An implementation of the paper FcaNet: Frequency Channel Attention Networks on CIFAR10/CIFAR100 dataset. how to run Code: python Cifar.py

76 Feb 04, 2021
Notebooks em Python para Métodos Eletromagnéticos

GeoSci Labs This is a repository of code used to power the notebooks and interactive examples for https://em.geosci.xyz and https://gpg.geosci.xyz. Th

Victor Cezar Tocantins 1 Nov 16, 2021
PyTorch implementation for paper StARformer: Transformer with State-Action-Reward Representations.

StARformer This repository contains the PyTorch implementation for our paper titled StARformer: Transformer with State-Action-Reward Representations.

Jinghuan Shang 14 Dec 09, 2022
we propose EfficientDerain for high-efficiency single-image deraining

EfficientDerain we propose EfficientDerain for high-efficiency single-image deraining Requirements python 3.6 pytorch 1.6.0 opencv-python 4.4.0.44 sci

Qing Guo 126 Dec 07, 2022
Official implementation of YOGO for Point-Cloud Processing

You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module By Chenfeng Xu, Bohan Zhai, Bichen Wu, T

Chenfeng Xu 67 Dec 20, 2022
Implementation for "Exploiting Aliasing for Manga Restoration" (CVPR 2021)

[CVPR Paper](To appear) | [Project Website](To appear) | BibTex Introduction As a popular entertainment art form, manga enriches the line drawings det

133 Dec 15, 2022
A crossplatform menu bar application using mpv as DLNA Media Renderer.

Macast Chinese README A menu bar application using mpv as DLNA Media Renderer. Install MacOS || Windows || Debian Download link: Macast release latest

4.4k Jan 01, 2023
Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+

Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+

567 Dec 26, 2022
Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Causality In Traffic Accident (Under Construction) Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020) Overview Data Prepa

Tackgeun 21 Nov 20, 2022
Code for the CVPR2021 paper "Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition"

Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition This repository contains code for the CVPR2021 paper "Patch-NetV

QVPR 368 Jan 06, 2023
Simulating an AI playing 2048 using the Expectimax algorithm

2048-expectimax Simulating an AI playing 2048 using the Expectimax algorithm The base game engine uses code from here. The AI player is modeled as a m

Subha Ramesh 2 Jan 31, 2022
level1-image-classification-level1-recsys-09 created by GitHub Classroom

level1-image-classification-level1-recsys-09 ❗ 주제 설명 COVID-19 Pandemic 상황 속 마스크 착용 유무 판단 시스템 구축 마스크 착용 여부, 성별, 나이 총 세가지 기준에 따라 총 18개의 class로 구분하는 모델 ?

6 Mar 17, 2022
Code for the paper "Offline Reinforcement Learning as One Big Sequence Modeling Problem"

Trajectory Transformer Code release for Offline Reinforcement Learning as One Big Sequence Modeling Problem. Installation All python dependencies are

Michael Janner 266 Dec 27, 2022
A Light CNN for Deep Face Representation with Noisy Labels

A Light CNN for Deep Face Representation with Noisy Labels Citation If you use our models, please cite the following paper: @article{wulight, title=

Alfred Xiang Wu 715 Nov 05, 2022
Learning kernels to maximize the power of MMD tests

Code for the paper "Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy" (arXiv:1611.04488; published at ICLR 2017), by Douga

Danica J. Sutherland 201 Dec 17, 2022