Examinando por Materia "RECONOCIMIENTO FACIAL"

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  • Proyecto final de Grado
    Autoenconders y análisis de componentes principales: propuesta de generación de ejemplos adversarios en el contexto de sistemas de reconocimiento facial
    (2021-10-11) Fuster, Marina; Vidaurreta, Ignacio Matías; Pierri, Alan
    "En el presente trabajo se propone una nueva metodología de generación de ejemplos adversarios, datos manipulados para confundir algoritmos de clasificación. El objetivo en este caso es vulnerar sistemas de reconocimiento facial con una estrategia que se basa en el uso de autoencoders, un tipo de redes neuronales, y análisis de componentes principales."
  • Proyecto final de Grado
    Controlling face’s frame generation in StyleGAN’s latent space operations: modifying faces to deceive our memory
    (2022) Roca, Agustín; Britos, Nicolás Ignacio
    "Innocence Project is a non-profitable organization that works in reducing wrongful convictions. In collaboration with El Laboratorio de Sueño y Memoria from Instituto Tecnológico de Buenos Aires (ITBA), they are studying human memory in the context offace identification. They have a strong hypothesis stating that human memory heavily relies in face’s frame to recognize faces. If this is proved, it could mean that face recognition in police lineups couldn’t be trusted, as they may lead to wrongful convictions. This study uses experiments in order to try to prove this using faces with different properties, such as eyes size, but maintaining its frame as much as possible."
  • Proyecto final de Grado
    Use of generative adversarial networks for the creation and manipulation of facial images in the context of studying false memories and its effects on wrongful conviction cases: implementation of StyleGAN’s generative image modeling and style mixing properties to design an interface for experimentation purposes
    (2021-11-16) Lozano, Jimena; Herrán Oyhanarte, Maite Mercedes; Ramele, Rodrigo
    l Laboratorio de Sueño y Memoria from Instituto Tecnológico de Buenos Aires (ITBA) studies the formation of false memories, and how these can be reduced or modified, and is in collaboration with the Innocence Project to investigate how these can lead to errors in convictions. From this research, the need arises to carry out experiments with human faces that are similar to each other, and how this similarity can result in the formation of false memories. In this project, we investigate a field of Artificial Intelligence (AI), Deep Learning, which can provide us with a solution to the generation of artificial faces. In particular, we implement a face generation model using a Generative Adversarial Network (GAN), with the aim of generating faces as realistic as possible, so that a human cannot distinguish them from real faces. StyleGAN, a particular implementation of the GAN network, was the chosen architecture, because in addition to producing images with high resolution quality, it presents a model that allows navigation of the latent space and the synthesis of faces, using style mixing properties. Finally, an application called FG-Style was developed and installed on a GPU-based server at ITBA so that the laboratory can have control over the face generation model, and over the generation of faces similar to a selected one, using StyleGAN’s style mixing properties to have a grip over the change of specific features of the generated faces."