THESIS AND DISSERTATIONS
2017 |
Rafael Henrique Vareto Face Recognition based on a Collection of Binary Classifiers Masters Thesis Federal University of Minas Gerais, 2017. Abstract | BibTeX | Tags: Artificial Neural Network, Face Verification, Machine Learning, Open-set Face Identification, Partial Least Squares, Support Vector Machine, Surveillance @mastersthesis{Vareto:2017:MSc,Face Recognition is one of the most relevant problems in computer vision as we consider its importance to areas such as surveillance, forensics and psychology. In fact, a real-world recognition system has to cope with several unseen individuals and determine either if a given face image is associated with a subject registered in a gallery of known individuals or if two given faces represent equivalent identities. In this work, not only we combine hashing functions, embedding of classifiers and response value histograms to estimate when probe samples belong to the gallery set, but we also extract relational features to model the relation between pair of faces to determine whether they are from the same person. Both proposed methods are evaluated on five datasets: FRGCv1, LFW, PubFig, PubFig83 and CNN VGGFace. Results are promising and show that our method continues effective for both open-set face identification and verification tasks regardless of the dataset difficulty. |
2017 |
Rafael Henrique Vareto Face Recognition based on a Collection of Binary Classifiers Masters Thesis Federal University of Minas Gerais, 2017. Abstract | BibTeX | Tags: Artificial Neural Network, Face Verification, Machine Learning, Open-set Face Identification, Partial Least Squares, Support Vector Machine, Surveillance @mastersthesis{Vareto:2017:MSc,Face Recognition is one of the most relevant problems in computer vision as we consider its importance to areas such as surveillance, forensics and psychology. In fact, a real-world recognition system has to cope with several unseen individuals and determine either if a given face image is associated with a subject registered in a gallery of known individuals or if two given faces represent equivalent identities. In this work, not only we combine hashing functions, embedding of classifiers and response value histograms to estimate when probe samples belong to the gallery set, but we also extract relational features to model the relation between pair of faces to determine whether they are from the same person. Both proposed methods are evaluated on five datasets: FRGCv1, LFW, PubFig, PubFig83 and CNN VGGFace. Results are promising and show that our method continues effective for both open-set face identification and verification tasks regardless of the dataset difficulty. |