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Title: Biometría facial con Deep Learning. Reconocimiento facial con redes neuronales convolucionales
Author: Muñoz Plá, Manuel
Tutor: Hernández Jiménez, Enric
Others: Isern-Deya, Andreu Pere  
Abstract: Facial biometrics is a technology capable of collecting biometric data from a face. Although both biometrics and neural networks have their theoretical roots in the mid-20th century, their widespread implementation and use have only been possible in recent years. The remarkable increase in computational power and the wealth of data now available have facilitated the widespread use of neural networks. Facial biometrics needs to measure physiological characteristics to differentiate a face from the rest of the image. This involves the collection of a large number of features (data). Analyzing large amounts of data, which in the case of images are unstructured data, is not an easy task. In the last decade, Deep Learning (DL) has turned image analysis on its head, where the performance of Convolutional Neural Networks (ConvNet) has surpassed all previous techniques, achieving great success in the detection and differentiation of objects in an image. For all these reasons, ConvNets have taken the lead in the image data processing. Once a face has been found, through facial biometrics, in addition to detecting and/or recognizing human face/s in an image or video, a lot of associated information can be processed: identification, emotions, video tracking, facial reconstructions, and many others. This paper focuses on Facial Recognition because it brings together most of the processes and data processing needed in facial biometrics. A review of the 'State of the Art in Facial Recognition' and a proof of concept: 'Facial Detection and Recognition through a webcam' is performed.
Keywords: deep learning
facial recognition
convolutional neural network
Document type: info:eu-repo/semantics/masterThesis
Issue Date: Jan-2023
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Appears in Collections:Trabajos finales de carrera, trabajos de investigación, etc.

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