Desarrollo de una aplicación para ordenadores para la detección y reconocimiento facial de alumnos para el posterior control de asistencia

El principal objetivo de este trabajo, es el desarrollo de una aplicación compilada, mediante la interfaz gráfica de Matlab (GUI), que sea capaz, previo aprendizaje de la aplicación, de localizar la cara en la fotografía, recortarla para eliminar espacio de fondo, y posteriormente reconocer a la persona en cuestión, si está en la base de datos. Si la persona no estuviera, el programa también sería capaz de decirlo. Por último, el programa realizará un volcado de los datos de las personas reconocidas en un fichero de texto a modo de registro, para así satisfacer el fin último de este proyecto, que pretendía ser usado para un posible control de asistencia a clase. Se han estudiado y utilizado varios métodos y algoritmos previamente programados en las librerías de Matlab, eligiendo el método óptimo para el mayor número de reconocimientos. El algoritmo finalmente elegido es el EigenFaces.The aim of this work, it is to develop a compiled application, using Matlab’s graphical interface (GUI), which it is capable, with the previous learning of the program, of locate a face in the photograph, to crop the face, in order to erase the background of the photo, and later to recognize the person in question, if that person is in the database. If he/she is not in the database, the program will be able to say it. Finally, the program will pass all the log in information to a text file, like a sort of register, to satisfy the finally aim of this work, which was trying to implement a control of assistance for class. Several methods and algorithms stored in Matlab’s libraries were studied and used, choosing the ideal one for the major number of recognitions. The chosen algorithm is the EigenFaces.Álvarez López, G. (2016). Desarrollo de una aplicación para ordenadores para la detección y reconocimiento facial de alumnos para el posterior control de asistencia. Universitat Politècnica de València. http://hdl.handle.net/10251/73415TFG

Biometric fusion methods for adaptive face recognition in computer vision

PhD ThesisFace recognition is a biometric method that uses different techniques to identify the individuals based on the facial information received from digital image data. The system of face recognition is widely used for security purposes, which has challenging problems. The solutions to some of the most important challenges are proposed in this study. The aim of this thesis is to investigate face recognition across pose problem based on the image parameters of camera calibration. In this thesis, three novel methods have been derived to address the challenges of face recognition and offer solutions to infer the camera parameters from images using a geomtric approach based on perspective projection. The following techniques were used: camera calibration CMT and Face Quadtree Decomposition (FQD), in order to develop the face camera measurement technique (FCMT) for human facial recognition. Facial information from a feature extraction and identity-matching algorithm has been created. The success and efficacy of the proposed algorithm are analysed in terms of robustness to noise, the accuracy of distance measurement, and face recognition. To overcome the intrinsic and extrinsic parameters of camera calibration parameters, a novel technique has been developed based on perspective projection, which uses different geometrical shapes to calibrate the camera. The parameters used in novel measurement technique CMT that enables the system to infer the real distance for regular and irregular objects from the 2-D images. The proposed system of CMT feeds into FQD to measure the distance between the facial points. Quadtree decomposition enhances the representation of edges and other singularities along curves of the face, and thus improves directional features from face detection across face pose. The proposed FCMT system is the new combination of CMT and FQD to recognise the faces in the various pose. The theoretical foundation of the proposed solutions has been thoroughly developed and discussed in detail. The results show that the proposed algorithms outperform existing algorithms in face recognition, with a 2.5% improvement in main error recognition rate compared with recent studies

Face recognition in uncontrolled environments

This thesis concerns face recognition in uncontrolled environments in which the images used for training and test are collected from the real world instead of laboratories. Compared with controlled environments, images from uncontrolled environments contain more variation in pose, lighting, expression, occlusion, background, image quality, scale, and makeup. Therefore, face recognition in uncontrolled environments is much more challenging than in controlled conditions. Moreover, many real world applications require good recognition performance in uncontrolled environments. Example applications include social networking, human-computer interaction and electronic entertainment. Therefore, researchers and companies have shifted their interest from controlled environments to uncontrolled environments over the past seven years. In this thesis, we divide the history of face recognition into four stages and list the main problems and algorithms at each stage. We find that face recognition in unconstrained environments is still an unsolved problem although many face recognition algorithms have been proposed in the last decade. Existing approaches have two major limitations. First, many methods do not perform well when tested in uncontrolled databases even when all the faces are close to frontal. Second, most current algorithms cannot handle large pose variation, which has become a bottleneck for improving performance. In this thesis, we investigate Bayesian models for face recognition. Our contributions extend Probabilistic Linear Discriminant Analysis (PLDA) [Prince and Elder 2007]. In PLDA, images are described as a sum of signal and noise components. Each component is a weighted combination of basis functions. We firstly investigate the effect of degree of the localization of these basis functions and find better performance is obtained when the signal is treated more locally and the noise more globally. We call this new algorithm multi-scale PLDA and our experiments show it can handle lighting variation better than PLDA but fails for pose variation. We then analyze three existing Bayesian face recognition algorithms and combine the advantages of PLDA and the Joint Bayesian Face algorithm [Chen et al. 2012] to propose Joint PLDA. We find that our new algorithm improves performance compared to existing Bayesian face recognition algorithms. Finally, we propose Tied Joint Bayesian Face algorithm and Tied Joint PLDA to address large pose variations in the data, which drastically decreases performance in most existing face recognition algorithms. To provide sufficient training images with large pose difference, we introduce a new database called the UCL Multi-pose database. We demonstrate that our Bayesian models improve face recognition performance when the pose of the face images varies