Deteção de face falsa com imagem NIR multiespectral e proposta de sistema biométrico facial para controle de presença

Abstract

Presence control systems that use perform face authentication need fraud detectors more reliable. A system to able to detect this task automatically and correctly brings a number of practical advantages in the field of biometric authentication. For this problem, an anti-spoofing is developed and serves as a pre-step before face recognition. The proposed approach for false face detection is to use NIR infrared camera and machine learning with deep learning. In this dissertation, it was created a database of fake and real face images with an infrared camera. From the images, three datasets were created to implement the machine learning models: Decision Tree, Random Forest, KNN, SVM and MLP. For the construction of the face recognition prototype with anti-spoofing, the Python programming language, the OpenFace, Scikit-Learn, OpenCV and Flask programming libraries were used. From these trained tools and models it was possible to have an accuracy of 97.50% for detection of false faces and real faces with the SVM classifier. For face recognition, a reliable threshold (from 0 to 1) of 0.6 for systems using 1 to N format authentication and 0.25 to 1 to 1 format threshold is set. It is intended that the proposed prototype be tested on a network of attendance at IPB.