Incremental and Decremental Nonparametric Discriminant Analysis for Face Recognition

Nonparametric Discriminant Analysis (NDA) possesses inherent advantages over Linear Discriminant Analysis (LDA) such as capturing the boundary structure of samples and avoiding matrix inversion. In this paper, we present a novel method for constructing an updated Nonparametric Discriminant Analysis (NDA) model for face recognition. The proposed method is applicable to scenarios where bursts of data samples are added to the existing model in random chunks. Also, the samples which degrade the performance of the model need to be removed. For both of these problems, we propose incremental NDA (INDA) and decremental NDA (DNDA) respectively. Experimental results on four publicly available datasets viz. AR, PIE, ORL and Yale show the efficacy of the proposed method. Also, the proposed method requires less computation time in comparison to batch NDA which makes it suitable for real time applications

Incremental construction of classifier and discriminant ensembles

We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role of search direction. For discriminant ensembles, we test subset selection and trees. Fusion is by voting or by a linear model. Using 14 classifiers on 38 data sets. incremental search finds small, accurate ensembles in polynomial time. The discriminant ensemble uses a subset of discriminants and is simpler, interpretable, and accurate. We see that an incremental ensemble has higher accuracy than bagging and random subspace method; and it has a comparable accuracy to AdaBoost. but fewer classifiers.We would like to thank the three anonymous referees and the editor for their constructive comments, pointers to related literature, and pertinent questions which allowed us to better situate our work as well as organize the ms and improve the presentation. This work has been supported by the Turkish Academy of Sciences in the framework of the Young Scientist Award Program (EA-TUBA-GEBIP/2001-1-1), Bogazici University Scientific Research Project 05HA101 and Turkish Scientific Technical Research Council TUBITAK EEEAG 104EO79Publisher's VersionAuthor Pre-Prin

Mitigating the effect of covariates in face recognition

Current face recognition systems capture faces of cooperative individuals in controlled environment as part of the face recognition process. It is therefore possible to control lighting, pose, background, and quality of images. However, in a real world application, we have to deal with both ideal and imperfect data. Performance of current face recognition systems is affected for such non-ideal and challenging cases. This research focuses on designing algorithms to mitigate the effect of covariates in face recognition.;To address the challenge of facial aging, an age transformation algorithm is proposed that registers two face images and minimizes the aging variations. Unlike the conventional method, the gallery face image is transformed with respect to the probe face image and facial features are extracted from the registered gallery and probe face images. The variations due to disguises cause change in visual perception, alter actual data, make pertinent facial information disappear, mask features to varying degrees, or introduce extraneous artifacts in the face image. To recognize face images with variations due to age progression and disguises, a granular face verification approach is designed which uses dynamic feed-forward neural architecture to extract 2D log polar Gabor phase features at different granularity levels. The granular levels provide non-disjoint spatial information which is combined using the proposed likelihood ratio based Support Vector Machine match score fusion algorithm. The face verification algorithm is validated using five face databases including the Notre Dame face database, FG-Net face database and three disguise face databases.;The information in visible spectrum images is compromised due to improper illumination whereas infrared images provide invariance to illumination and expression. A multispectral face image fusion algorithm is proposed to address the variations in illumination. The Support Vector Machine based image fusion algorithm learns the properties of the multispectral face images at different resolution and granularity levels to determine optimal information and combines them to generate a fused image. Experiments on the Equinox and Notre Dame multispectral face databases show that the proposed algorithm outperforms existing algorithms. We next propose a face mosaicing algorithm to address the challenge due to pose variations. The mosaicing algorithm generates a composite face image during enrollment using the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a users face image. Experiments conducted on three different databases indicate that face mosaicing offers significant benefits by accounting for the pose variations that are commonly observed in face images.;Finally, the concept of online learning is introduced to address the problem of classifier re-training and update. A learning scheme for Support Vector Machine is designed to train the classifier in online mode. This enables the classifier to update the decision hyperplane in order to account for the newly enrolled subjects. On a heterogeneous near infrared face database, the case study using Principal Component Analysis and C2 feature algorithms shows that the proposed online classifier significantly improves the verification performance both in terms of accuracy and computational time

Extracción de características mediante criterios basados en teoría de la información

El objetivo de esta tesis es desarrollar nuevas técnicas de extracción lineal de características para clasificación, mediante el uso de criterios basados en teoría de la información. Dado que esta teoría establece un marco que permite medir el grado de dependencia entre variables aleatorias o señales, nos proporciona una buena medida del nivel de relevancia de las características extraídas respecto a la clase a la que pertenece cada muestra. Se exploran dos aproximaciones distintas. La primera de ellas está basada en funciones de contraste que aproximan la información mutua entre las proyecciones obtenidas individualmente y las clases. En la segunda, se plantea como criterio a optimizar el test de hipótesis que sirve como regla de clasificación. La aproximación basada en funciones de contraste hace uso de una estimación de la información mutua similar a otra que ha sido usada con éxito en análisis de componentes independientes. La estimación utiliza momentos sobre funciones no polinomiales aplicadas a los datos. De esta forma, no es necesario el modelo de la función densidad de probabilidad (FDP) de los datos. Al ser derivables estas funciones, es posible su optimización mediante ascenso por gradiente. El método basado en la maximización del test de hipótesis hace uso de un modelo de Parzen para estimar la FDP de los datos. Se propone un algoritmo para optimizar dichos modelos de acuerdo con el criterio de máxima verosimilitud combinado con un procedimiento de validación cruzada. Su aplicación al problema de extracción lineal de características consiste en la maximización del cociente de verosimilitudes medido sobre los datos de entrenamiento. También se describen procedimientos para la aplicación de estos modelos optimizados a otros problemas de aprendizaje como análisis de componentes independientes y clasifiación de Parzen. Junto a los métodos propuestos, se presentan los resultados de la aplicación de los mismos a problemas reales, registrados en bases de datos públicas. La tesis finaliza con las conclusiones más relevantes que pueden extraerse del trabajo presentado, así como la propuesta de líneas de trabajo que constituyen la continuación natural de las ya expuestasThe objective of this PhD Thesis is to develop new linear feature extraction methods for classification, by the use of criteria from Information Theory. This theory allows us to measure the statistical dependence among random variables or signals; in a classification context, the measure of interest is the relevance of the feature extracted with respect to the labels. Two different methodologies are explored: the first one is based on contrast functions that approximate the mutual information between the set of projections obtained and the classes. The second approach proposes a hypothesis test scheme as a rule for optimal classification performance. The approach based on contrast functions makes use of a mutual information estimation related to another one that has been applied to Independent Component Analysis (ICA) with success. The estimation is based on non polynomial moments obtained from data. This way, the probability density function (PDF) need not to be estimated. As the non polynomial functions are derivable, an optimization procedure by gradient ascent is applied. The approach based on the hypothesis test maximization does make use of the PDF from the data, by means of Parzen non-parametric models, also known as kernel density estimators. To optimize these models, an algorithm is proposed for determining the optimal window width by a maximum likelihood criterion, combined with a cross validation procedure. The application of these models to the feature extraction problem is based on the maximization of the hypothesis test among classes estimated from the training set, using the features extracted. In addition to feature extraction, the Thesis proposes methods for the application of these optimized PDF modelos to other machine learning problems as ICA and Parzen classification. Simulation results are provided, in which the methods proposed are applied to real problems, recorded on public datasets. The Thesis finishes with some conclusions about the advantages and weaknesses of each procedure. Some suggestions are also made about frameworks that can be considered as the natural continuation of the work presente

Explain what you see:argumentation-based learning and robotic vision

In this thesis, we have introduced new techniques for the problems of open-ended learning, online incremental learning, and explainable learning. These methods have applications in the classification of tabular data, 3D object category recognition, and 3D object parts segmentation. We have utilized argumentation theory and probability theory to develop these methods. The first proposed open-ended online incremental learning approach is Argumentation-Based online incremental Learning (ABL). ABL works with tabular data and can learn with a small number of learning instances using an abstract argumentation framework and bipolar argumentation framework. It has a higher learning speed than state-of-the-art online incremental techniques. However, it has high computational complexity. We have addressed this problem by introducing Accelerated Argumentation-Based Learning (AABL). AABL uses only an abstract argumentation framework and uses two strategies to accelerate the learning process and reduce the complexity. The second proposed open-ended online incremental learning approach is the Local Hierarchical Dirichlet Process (Local-HDP). Local-HDP aims at addressing two problems of open-ended category recognition of 3D objects and segmenting 3D object parts. We have utilized Local-HDP for the task of object part segmentation in combination with AABL to achieve an interpretable model to explain why a certain 3D object belongs to a certain category. The explanations of this model tell a user that a certain object has specific object parts that look like a set of the typical parts of certain categories. Moreover, integrating AABL and Local-HDP leads to a model that can handle a high degree of occlusion

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

State discovery for autonomous learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (p. 163-171).This thesis is devoted to the study of algorithms for early perceptual learning for an autonomous agent in the presence of feedback. In the framework of associative perceptual learning with indirect supervision, three learning techniques are examined in detail: * short-term on-line memory-based model learning; * long-term on-line distribution-based statistical estimation; * mixed on- and off-line continuous learning of gesture models. The three methods proceed within essentially the same framework, consisting of a perceptual sub-system and a sub-system that implements the associative mapping from perceptual categories to actions. The thesis contributes in several areas - it formulates the framework for solving incremental associative learning tasks; introduces the idea of incremental classification with utility, margin and boundary compression rules; develops a technique of sequence classification with Support Vector Machines; introduces an idea of weak transduction and offers an EM-based algorithm for solving it; proposes a mixed on- and off-line algorithm for learning continuous gesture with reward-based decomposition of the state space. The proposed framework facilitates the development of agents and human-computer interfaces that can be trained by a naive user. The work presented in this dissertation focuses on making these incremental learning algorithms practical.by Yuri A. Ivanov.Ph.D

Generalizing, Decoding, and Optimizing Support Vector Machine Classification

The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification. Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms