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Advisor(s)
Abstract(s)
Os sistemas de controle de presenças que realizam a autenticação através de faces carecem
de detectores de fraudes para que sejam mais confiáveis. Um sistema capaz de executar
essa tarefa automaticamente e corretamente vem trazer uma série de vantagens práticas no
domínio da autenticação biométrica. Para atender esta carência, um detector de face falsa
é desenvolvido e serve como um pré-passo antes do reconhecimento facial. A abordagem
proposta para detecção de face falsa é utilizar câmera infravermelha do espectro NIR e
machine learning, referida de deep learning. Neste trabalho foi criado uma base de dados
de imagens de faces falsas e reais com auxílio de uma câmera com luz infravermelha
NIR. A partir das imagens, foram gerados três datasets para implementação dos modelos
de machine learning: Árvore de Decisão, Random Forest, KNN, SVM e MLP. Para a
construção do protótipo de reconhecimento facial com detector de face falsa foi utilizado
a linguagem Python de programação, as bibliotecas de programação: OpenFace, Scikit-
Learn, OpenCV e Flask. A partir destas ferramentas e modelos treinados foi possível
ter uma acurácia de 97.50% para detecção de faces falsas e faces reais com o classificador
SVM. Para o reconhecimento facial foi definido uma limiar (de 0 a 1) confiável de 0.6 para
sistemas que utilizam autenticação no formato 1 para N e limiar 0.2 para formato 1 para
1. Pretende-se que no futuro, o protótipo proposto seja ensaiado numa rede de terminais
de marcação de presenças no IPB.
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.
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.
Description
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do Paraná
Keywords
Reconhecimento facial Detecção de fraudes Machine learning Deep learning