[[alternative]]Content Based Painting Image Retrieval

計畫編號：NSC92-2213-E032-022研究期間：200308~200407研究經費：409,000[[sponsorship]]行政院國家科學委員

Face Verification without False Acceptance

Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are two popular approaches in face recognition and verification. The methods are classified under appearance-based approach and are considered to be highly-correlated. The last factor deems a fusion of both methods to be unfavorable. Nevertheless the authors will demonstrate a verification performance in which the fusion of both method produces an improved rate compared to individual performance. Tests are carried out on FERET (Facial Recognition Technology) database using a modified protocol. A major drawback in applying LDA is that it requires a large set of individual face images sample to extract the intra-class variations. In real life application data enrolment incurs costs such as human time and hardware setup. Tests are therefore conducted using virtual images and its performance and behaviour recorded as an option for multiple sample. The FERET database is chosen because it is widely used by researchers and published results are available for comparisons. Performance is presented as the rate of verification when false acceptance rate is zero, in other words, no impostors allowed. Initial results using fusion of two verification experts shows that a fusion of T-Zone LDA with Gabor LDA of whole face produces the best verification rate of 98.2% which is over 2% improvement compared with the best individual expert

A New Design of Multiple Classifier System and its Application to Classification of Time Series Data

To solve the challenging pattern classification problem, machine learning researchers have extensively studied Multiple Classifier Systems (MCSs). The motivations for combining classifiers are found in the literature from the statistical, computational and representational perspectives. Although the results of classifier combination does not always outperform the best individual classifier in the ensemble, empirical studies have demonstrated its superiority for various applications. A number of viable methods to design MCSs have been developed including bagging, adaboost, rotation forest, and random subspace. They have been successfully applied to solve various tasks. Currently, most of the research is being conducted on the behavior patterns of the base classifiers in the ensemble. However, a discussion from the learning point of view may provide insights into the robust design of MCSs. In this thesis, Generalized Exhaustive Search and Aggregation (GESA) method is developed for this objective. Robust performance is achieved using GESA by dynamically adjusting the trade-off between fitting the training data adequately and preventing the overfitting problem. Besides its learning algorithm, GESA is also distinguished from traditional designs by its architecture and level of decision-making. GESA generates a collection of ensembles and dynamically selects the most appropriate ensemble for decision-making at the local level. Although GESA provides a good improvement over traditional approaches, it is not very data-adaptive. A data- adaptive design of MCSs demands that the system can adaptively select representations and classifiers to generate effective decisions for aggregation. Another weakness of GESA is its high computation cost which prevents it from being scaled to large ensembles. Generalized Adaptive Ensemble Generation and Aggregation (GAEGA) is an extension of GESA to overcome these two difficulties. GAEGA employs a greedy algorithm to adaptively select the most effective representations and classifiers while excluding the noise ones as much as possible. Consequently, GAEGA can generate fewer ensembles and significantly reduce the computation cost. Bootstrapped Adaptive Ensemble Generation and Aggregation (BAEGA) is another extension of GESA, which is similar with GAEGA in the ensemble generation and decision aggregation. BAEGA adopts a different data manipulation strategy to improve the diversity of the generated ensembles and utilize the information in the data more effectively. As a specific application, the classification of time series data is chosen for the research reported in this thesis. This type of data contains dynamic information and proves to be more complex than others. Multiple Input Representation-Adaptive Ensemble Generation and Aggregation (MIR-AEGA) is derived from GAEGA for the classification of time series data. MIR-AEGA involves some novel representation methods that proved to be effective for time series data. All the proposed methods including GESA, GAEGA, MIR-AEGA, and BAEGA are tested on simulated and benchmark data sets from popular data repositories. The experimental results confirm that the newly developed methods are effective and efficient

Multiple classifier combination for face-based identity verification

When combining outputs from multiple classifiers, many combination rules are available. Although easy to implement, fixed combination rules are optimal only in restrictive conditions. We discuss and evaluate their performance when the optimality conditions are not fulfilled. Fixed combination rules are then compared with trainable combination rules on real data in the context of face-based identity verification. The face images are classified by combining the outputs of five different face verification experts. It is demonstrated that a reduction in the error rates of up to 50% over the best single expert is achieved on the XM2VTS database, using either fixed or trainable combination rules. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved

Optimising multimodal fusion for biometric identification systems

Biometric systems are automatic means for imitating the human brain’s ability of identifying and verifying other humans by their behavioural and physiological characteristics. A system, which uses more than one biometric modality at the same time, is known as a multimodal system. Multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. This thesis addresses some issues related to the implementation of multimodal biometric identity verification systems. The thesis assesses the feasibility of using commercial offthe-shelf products to construct deployable multimodal biometric system. It also identifies multimodal biometric fusion as a challenging optimisation problem when one considers the presence of several configurations and settings, in particular the verification thresholds adopted by each biometric device and the decision fusion algorithm implemented for a particular configuration. The thesis proposes a novel approach for the optimisation of multimodal biometric systems based on the use of genetic algorithms for solving some of the problems associated with the different settings. The proposed optimisation method also addresses some of the problems associated with score normalization. In addition, the thesis presents an analysis of the performance of different fusion rules when characterising the system users as sheep, goats, lambs and wolves. The results presented indicate that the proposed optimisation method can be used to solve the problems associated with threshold settings. This clearly demonstrates a valuable potential strategy that can be used to set a priori thresholds of the different biometric devices before using them. The proposed optimisation architecture addressed the problem of score normalisation, which makes it an effective “plug-and-play” design philosophy to system implementation. The results also indicate that the optimisation approach can be used for effectively determining the weight settings, which is used in many applications for varying the relative importance of the different performance parameters

Multiple Classifier Combination for Face-Based Identity Verification

When combining outputs from multiple classifiers, many combination rules are available. Although easy to implement, fixed combination rules are optimal only in restrictive conditions. We discuss and evaluate their performance when the optimality conditions are not fulfilled. Fixed combination rules are then compared with trainable combination rules on real data in the context of face-based identity verification. The face images are classified by combining the outputs of five different face verification experts. It is demonstrated that a reduction in the error rates of up to 50% over the best single expert is achieved on the XM2VTS database, using either fixed or trainable combination rules