Ing Ren Tsang

Feature representation selection based on Classifier Projection Space and Oracle analysis

One of the main problems in pattern recognition is obtaining the best set of features to represent the data. In recent years, several feature extraction algorithms have been proposed. However, due to the high degree of variability of the patterns, it is difficult to design a single representation that can capture the complex structure of the data. One possible solution to this problem is to use a multiple-classifier system (MCS) based on multiple feature representations. Unfortunately, still missing in the literature is a methodology for comparing and selecting feature extraction techniques based on the dissimilarity of the feature representations. In this paper, we propose a framework based on dissimilarity metrics and the intersection of errors, in order to analyze the relationships among feature representations. Each representation is used to train a classifier, and the results are compared by means of a dissimilarity metric. Then, with the aid of Multidimensional Scaling, visual representations are obtained of each of the dissimilarities and used as a guide to identify those that are either complementary or redundant. We applied the proposed framework to the problem of handwritten character and digit recognition. The analysis is followed by the use of an MCS built on the assumption that combining dissimilar feature representations can greatly improve the performance of the system. Experimental results demonstrate that a significant improvement in classification accuracy is achieved due to the complementary nature of the representations. Moreover, the proposed MCS obtained the best results to date for both the MNIST handwritten digit dataset and the Cursive Character Challenge (C-Cube) dataset.

Lag selection for time series forecasting using Particle Swarm Optimization

The time series forecasting is an useful application for many areas of knowledge such as biology, economics, climatology, biology, among others. A very important step for time series prediction is the correct selection of the past observations (lags). This paper uses a new algorithm based in swarm of particles to feature selection on time series, the algorithm used was Frankensteins Particle Swarm Optimization (FPSO). Many forms of filters and wrappers were proposed to feature selection, but these approaches have their limitations in relation to properties of the data set, such as size and whether they are linear or not. Optimization algorithms, such as FPSO, make no assumption about the data and converge faster. Hence, the FPSO may to find a good set of lags for time series forecasting and produce most accurate forecastings. Two prediction models were used: Multilayer Perceptron neural network (MLP) and Support Vector Regression (SVR). The results show that the approach improved previous results and that the forecasting using SVR produced best results, moreover its showed that the feature selection with FPSO was better than the features selection with original Particle Swarm Optimization.

Speaker verification using type-2 Fuzzy Gaussian Mixture Models

This paper proposes the use of a Type-2 Fuzzy GMM (T2FGMM) speaker verification system for mobile devices in variable environments. This model is an extension of Gaussian mixture models based on the type-2 fuzzy set, which provide pertinence intervals for the trained samples. The decision process is obtained using the Generalized Linear Model (GLM) that processes the interval likelihoods. A Voice Activity Detection (VAD) algorithm was also used to improve the speaker verification ratio. The proposed method was tested on the MIT mobile device speaker verification database which contains several different mobile devices used in different environments. The result shows the robustness of the system and the improvements in the verification ratios of the T2GMM over the classical GMM.

Pupil segmentation using Pulling & Pushing and BSOM neural network

Segmentation is a preliminary step for many computer vision systems. Several segmentation algorithms have been developed for different tasks. Here, we are interested in the pupil segmentation, an important procedure in iris recognition systems. In most of the pupil segmentation algorithms it is assumed that the pupil has a circular shape. These methods inaccurate identify pupil borders that do not have a circular shape. In iris recognition, the error caused by an imprecise segmentation can lead to poor recognition rates. In this paper we propose a new method for pupil segmentation based on the Pulling & Pushing method and a batch-SOM neural network in order to improve the segmentation. We tested the proposed method in the MMU1 and Casia V3 iris databases, obtaining accurate results.

A robust feature extraction algorithm based on class-Modular Image Principal Component Analysis for face verification

Face verification systems reach good performance on ideal environmental conditions. Conversely, they are very sensitive to non-controlled environments. This work proposes the class-Modular Image Principal Component Analysis (cMIMPCA) algorithm for face verification. It extracts local and global information of the user faces aiming to reduce the effects caused by illumination, facial expression and head pose changes. Experimental results performed over three well-known face databases showed that cMIMPCA obtains promising results for the face verification task.

Combined AdaBoost and gradientfaces for face detection under illumination problems

Regardless of several different methods for face detection have been developed in the last years, there are still situations that requires more improvements especially in issues related to variations in illumination and face occlusion. Illumination problems are normally handled by using preprocessing, and model or training-based approaches. We propose here a face detection method combining the well-known AdaBoost with Gradientfaces following a model-based approach, which was not yet used for the face detection problem. We have applied Gradientfaces before training an AdaBoost Haar-based cascade classifier to overcome the problem of strong variations in illumination. Cited approaches were evaluated first in a data set containing artificial and then real illumination problems. Experiments show that proposed method is stable when facing different lighting conditions, and better than others when dealing with strong and uncontrolled illumination problems.

MLPBoost: A combined AdaBoost / multi-layer perceptron network approach for face detection

Face detection is a research area in computer vision of great interest. Even though several different methods have been developed, improvements can still be made in the false-positive detection and increase in the speed of the detector. In this work, we investigate the AdaBoost technique as an artificial neural network. We propose a new model called MLPBoost, which is an hybridization between AdaBoost and Multi-Layer Perceptron (MLP) networks. This algorithm has shown improvements in the performance of classifiers already trained with AdaBoost, either by the increase in the detection rate and the reduction of false positive rates, or by decreasing the processing time of these classifiers.

Supervised fractional eigenfaces.

Supervised Fractional Eigenfaces (SFE) is an extension of Principal Component Analysis (PCA), which uses the fractional covariance matrix, class label information, and nonlinear data transformation to extract discriminant features. The proposed method combines techniques of two state-of-the-art feature extractors: Fractional Eigenfaces and Dual Supervised PCA. Supervised Fractional Eigenfaces was evaluated in three known face datasets and it achieved significant smaller recognition error.

Recognition of partially occluded face using Gradientface and Local Binary Patterns

Currently one of the most important challenges of face recognition systems is the problem of occlusion, which is quite common in real applications. There are several studies in the literature treating this problem, but no defined or robust solution is agreed. The focus of this work is to develop face recognition method with sunglasses and scarf occlusion. We propose a robust approach which consists in detecting the face region that does not have occlusion and uses this region to obtain the recognition. To classify the occluded and non-occluded parts, a Multi-Layer Perceptron (MLP) is applied. While for the recognition a combined Gradientface and Local Binary Pattern (LBP) are used. Gradientface is applied to address the variation in the illumination of the image. Experiments are shown using the AR Face and ORL databases.

Combining dissimilarity spaces for text categorization

Text categorization systems are designed to classify documents into a fixed number of predefined categories. Bag-of-words is one of the most used approaches to represent a document. However, it generates high-dimensional sparse data matrix with a high feature-to-instance ratio. An aggressive feature selection can alleviate these drawbacks, but such selection degrades the classifiers performance. In this paper, we propose an approach for text categorization based on Dissimilarity Representation and multiple classifier systems. The proposed system, Combined Dissimilarity Spaces (CoDiS), is composed of multiple classifiers trained on data from different dissimilarity spaces. Each dissimilarity space is a transformation of the original space that reduces the dimensionality, feature-to-instance ratio, and sparseness. Experiments using forty-seven text categorization databases show that CoDiS presents a better performance in comparison to literature systems.