Head Tracking via Robust Registration in Texture Map Images

A novel method for 3D head tracking in the presence of large head rotations and facial expression changes is described. Tracking is formulated in terms of color image registration in the texture map of a 3D surface model. Model appearance is recursively updated via image mosaicking in the texture map as the head orientation varies. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. Parameters are estimated via a robust minimization procedure; this provides robustness to occlusions, wrinkles, shadows, and specular highlights. The system was tested on a variety of sequences taken with low quality, uncalibrated video cameras. Experimental results are reported

Automated Students Attendance System

The Automated Students' Attendance System is a system that takes the attendance of students in a class automatically. The system aims to improve the current attendance system that is done manually. This work presents the computerized system of automated students' attendance system to implement genetic algorithms in a face recognition system. The extraction of face template particularly the T-zone (symmetrical between the eyes, nose and mouth) is performed based on face detection using specific HSV colour space ranges followed by template matching. Two types of templates are used; one on edge detection and another on the intensity plane in YIQ colour space. Face recognition with genetic algorithms will be performed to achieve an automated students' attendance system. With the existence of this attendance system, the occurrence of truancy could be reduced tremendously

Incremental class representation learning for face recognition

Image classification is one of the most active challenging problems in computer vision field. Taking this to Deep Neural Networks with systems that are able to deal with large data sets as it can be ImageNet. Large Convolutional Networks as VGG-16 used in this work have recently demonstrated impressive classification performances. This work is focused on novel techniques for Incremental Learning stages for face recognition, which is an important open problem in artificial intelligence. The main challenge of this work is the development of incrementally learning systems that learn about more and more concepts over time. Most of the actual methods that use incremental learning in "online" or "offline" stages. This thesis focuses on "offline" incremental stages where the data available is distributed in batches of classes. Since the necessity to deal with a continuous training stages, some well-established methods for transfer learning are applied by the author to run the experiments. Preserving knowledge is the most challenge task to deal using incremental learning techniques. The actual research is on apply incremental learning in natural systems where for example, it is not considered to store all the old training data to make a new model when new data comes available. Another interesting concept for incremental learning systems is "lifelong" learning, which are related to the methods analyzed in this work since the system proposed also learn from a sequence of different tasks. The similarity of multi-task learning and "lifelong" learning is that they both use shared information across tasks to help learning, but also, multi-task learning is not able to grow the number of tasks over time preserving the knowledge.La clasificación de imágenes es una de las tareas más desafiantes en el campo de la visión por computador. Llevando esto al campo de las redes neuronales profundas utilizando sistemas que son capaces de gestionar datasets considerablemente grandes como puede ser ImageNet. Grandes redes convolucionales cómo puede ser VGG-16, que és la que se utilizarà en este trabajo, han demostrado muy buenos resultados. Este trabajo està focalizado en nuevas técnicas para aprendizaje incremental para el reconocimiento de caras, que \'{e}s un importante problema abierto en la inteligencia artificial. El mayor reto en este trabajo consiste en desarrollar dos sistemas incrementales que aprenden más conceptos a medida que pasa el tiempo. Muchos de estos métodos que utilizan el aprendizaje incremental en escenarios como "online" o "offline". Este trabajo está focalizado sobre todo en los sistemas incrementales que utilizan "offline" como método incremental de aprendizaje donde los datos son proporcionados por conjuntos separados de classes. Hay una necesidad clara de gestionar escenarios de aprendizaje continuo, y es por este motivo que métodos de transferencia de aprendizaje han estado estudiados y implementados por el autor del proyecto para tal de llevar a cabo la ejecución de experimentos. Una de las tascas más desafiantes es cómo gestionar y preservar el conocimiento obtenido para no olvidar. Cuando se habla de aprendizaje incremental, muchas veces va relacionado con el concepto de sistemas naturales donde por ejemplo, no esté contemplada la opci\'{o}n todas las muestras para el conocimiento adquirido para un futuro entrenamiento cuando haya clases disponibles para hacerlo. En cambio, el aprendizaje "online", se diferencia del "offline" durante el proceso de entrenamiento. Dónde se encarga de aprender de forma eficiente con datos que llegan de una forma incremental pero siempre corresponden a las mismas clases, dicho de otro modo, los sistemas que utilizan el aprendizaje "online" en la mayoría de trabajos propuestos, no se encargan de incrementar el nombre de clases. Otro concepto interesante para los sistemas de aprendizaje incremental es lo que se llama aprendizaje "lifelong", que también está relacionado con los métodos analizados en este trabajo, ya que el sistema propuesto también aprende de una secuencia de tascas distintas. También hay una similitud entre el aprendizaje para múltiples tascas y el aprendizaje "lifelong", que es que los dos métodos utilizan información compartida entre tascas para ayudar en el aprendizaje, de todas formas, los sistemas de aprendizaje para múltiples tascas tampoco puede augmentar el nombre de clases.La classificació d'imatges és una de les tasques més desafiants en el camp de la visi\'{o} per a computadors. Portant això al camp de les xarxes neuronals profundes utilitzant sistemes que s\'{o}n capa\c{c}os de gestionar datasets considerablement grans, com pot ser el de ImageNet. Grans xarxes convolucionals com pot ser VGG-16, que \'{e}s la que s'utilitzarà en aquest treball i que ha demostrat molt bons resultats. Aquest treball està focalitzat en noves tàcniques per aprenentatge incremental per al reconeixament de cares, que és un important problema obert en la inteligència artificial. El major repte en aquest treball és desenvolupar dos sistemes incrementals que aprenen més conceptes durant el temps. Molts dels actuals mètodes que utilitzen l'aprenentatje incrmental en escenaris com "online" o "offline". Aquest treball està focalitzat sobretot en els sistemes incrementals que utilitzen "offline" com a mètode incremental d'aprenentatge on les dades són proporcionades per conjunts de classes, on cada conjunt apareix en un moment diferent. Hi ha una necessitat per a gestionar amb escenaris d'aprenentatge continuu, i \'{e}s per això que mètodes de tranferència d'aprenentatje serán estudiats i implementats per l'autor del projecte per tal d'executar els experiments. Una de les tasques més desafiants és com gestionar i preservar el coneixament obtingut per tal de no oblidar. Quan es parla de aprenentatje incremental, molts cops està relacionat amb el concepte de sistemes naturals on per exemple, no està contemplada la possibilitat de guardar totes les mostres del coneixament adquirit per a un futur entrenament quan hi hagin noves classes disponibles. D'altra banda, l'aprenentatge "online" es diferencia del "offline" durant el procés d'entrenament. On s'encarrega d'apendre de forma eficient amb dades que arriben de forma incremental però sempre per les mateixes tasques, dit d'altre forma, els sistemes que utilitzen l'aprenentatge "online" en la majoria de treballs proposats, no s'encarreguen d'incrementar el nombre de classes. Un altre concepte interessant per als sistemes d'aprenentatge incremental és el que se'n diu aprenentatge lifelong, que també està relacionat amb els mètodes analitzats en aquest treball, ja que el sistema proposat també aprèn d'una seqüència de tasques diferents. També hi ha una similaritat entre l'aprenentatge per múltiples tasques i l'aprenentatge "lifelong", que és que els dos utilitzen informació compartida entre tasques per ajudar en l'aprenentatge, de totes formes, els sistemes d'aprenentatge per a múltiples tasques tampoc poden augmentar el nombre de classes

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images