Prabir Bhattacharya

Some remarks on fuzzy graphs

Abstract We show that a fuzzy group can be associated with a fuzzy graph in a natural way. Some properties of fuzzy graphs are considered and we introduce the notions of eccentricity and center. Our examples indicate that results from (crisp) graph theory do not always have analogs for fuzzy graphs.

A driver fatigue recognition model based on information fusion and dynamic Bayesian network

We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.

A Framework for Medical Image Retrieval Using Machine Learning and Statistical Similarity Matching Techniques With Relevance Feedback

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework

Fuzzy normal subgroups and fuzzy cosets

Abstract We introduce here the notion of 1. (i) a fuzzy normal subgroup and 2. (ii) a fuzzy coset. We prove that for a group G , a fuzzy subgroup is fuzzy normal if and only if it is constant on the conjugate classes of G . Further, we show that the level subgroups of a fuzzy normal subgroup are all normal. We obtain several results which are analogs of some basic theorems of group theory. In particular, a fuzzy version of Lagranges theorem is established.

Palm vein extraction and matching for personal authentication

In this paper, we propose a scheme of personal authentication using palm vein. The infrared palm images which contain the palm vein information are used for our system. Because the vein information represents the liveness of a human, this system can provide personal authentication and liveness detection concurrently. The proposed system include: 1) Infrared palm images capture; 2) Detection of Region of Interest; 3) Palm vein extraction by multiscale filtering; 4) Matching. The experimental results demonstrate that the recognition rate using palm vein is good.

Mechanism Design-Based Secure Leader Election Model for Intrusion Detection in MANET

In this paper, we study leader election in the presence of selfish nodes for intrusion detection in mobile ad hoc networks (MANETs). To balance the resource consumption among all nodes and prolong the lifetime of an MANET, nodes with the most remaining resources should be elected as the leaders. However, there are two main obstacles in achieving this goal. First, without incentives for serving others, a node might behave selfishly by lying about its remaining resources and avoiding being elected. Second, electing an optimal collection of leaders to minimize the overall resource consumption may incur a prohibitive performance overhead, if such an election requires flooding the network. To address the issue of selfish nodes, we present a solution based on mechanism design theory. More specifically, the solution provides nodes with incentives in the form of reputations to encourage nodes in honestly participating in the election process. The amount of incentives is based on the Vickrey, Clarke, and Groves (VCG) model to ensure truth-telling to be the dominant strategy for any node. To address the optimal election issue, we propose a series of local election algorithms that can lead to globally optimal election results with a low cost. We address these issues in two possible application settings, namely, Cluster-Dependent Leader Election (CDLE) and Cluster-Independent Leader Election (CILE). The former assumes given clusters of nodes, whereas the latter does not require any preclustering. Finally, we justify the effectiveness of the proposed schemes through extensive experiments.

A wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment

We present a wavelet-based, high performance, hierarchical scheme for image matching which includes (1) dynamic detection of interesting points as feature points at different levels of subband images via the wavelet transform, (2) adaptive thresholding selection based on compactness measures of fuzzy sets in image feature space, and (3) a guided searching strategy for the best matching from coarse level to fine level. In contrast to the traditional parallel approaches which rely on specialized parallel machines, we explored the potential of distributed systems for parallelism. The proposed image matching algorithms were implemented on a network of workstation clusters using parallel virtual machine (PVM). The results show that our wavelet-based hierarchical image matching scheme is efficient and effective for object recognition.

A game-theoretic intrusion detection model for mobile ad hoc networks

In this paper, we address the problem of increasing the effectiveness of an intrusion detection system (IDS) for a cluster of nodes in ad hoc networks. To reduce the performance overhead of the IDS, a leader node is usually elected to handle the intrusion detection service on behalf of the whole cluster. However, most current solutions elect a leader randomly without considering the resource level of nodes. Such a solution will cause nodes with less remaining resources to die faster, reducing the overall lifetime of the cluster. It is also vulnerable to selfish nodes who do not provide services to others while at the same time benefiting from such services. Our experiments show that the presence of selfish nodes can significantly reduce the effectiveness of an IDS because less packets are inspected over time. To increase the effectiveness of an IDS in MANET, we propose a unified framework that is able to: (1) Balance the resource consumption among all the nodes and thus increase the overall lifetime of a cluster by electing truthfully and efficiently the most cost-efficient node known as leader-IDS. A mechanism is designed using Vickrey, Clarke, and Groves (VCG) to achieve the desired goal. (2) Catch and punish a misbehaving leader through checkers that monitor the behavior of the leader. A cooperative game-theoretic model is proposed to analyze the interaction among checkers to reduce the false-positive rate. A multi-stage catch mechanism is also introduced to reduce the performance overhead of checkers. (3) Maximize the probability of detection for an elected leader to effectively execute the detection service. This is achieved by formulating a zero-sum non-cooperative game between the leader and intruder. We solve the game by finding the Bayesian Nash Equilibrium where the leaders optimal detection strategy is determined. Finally, empirical results are provided to support our solutions.

Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs

We present algorithms for iris segmentation, feature extraction and selection, and iris pattern matching. To segment the inner boundary from a nonideal iris image, we apply a level set based curve evolution approach using the edge stopping function, and to detect the outer boundary, we employ the curve evolution approach using the regularized Mumford-Shah segmentation model with an energy minimization algorithm. Daubechies wavelet transform (DBWT) is used to extract the textural features, and genetic algorithms (GAs) are deployed to select the subset of informative features by combining the valuable outcomes from the multiple feature selection criteria without compromising the recognition accuracy. To speed up the matching process and to control the misclassification error, we apply a combined approach called the adaptive asymmetrical support vector machines (AASVMs). The parameter values of SVMs are also optimized in order to improve the overall generalization performance. The verification and identification performance of the proposed scheme is validated using the UBIRIS Version 2, the ICE 2005, and the WVU datasets.

Detection of Facial Feature Points Using Anthropometric Face Model

This chapter describes an automated technique for detecting the eighteen most important facial feature points using a statistically developed anthropometric face model. Most of the important facial feature points are located just about the area of mouth, nose, eyes and eyebrows. After carefully observing the structural symmetry of human face and performing necessary anthropometric measurements, we have been able to construct a model that can be used in isolating the above mentioned facial feature regions. In the proposed model, distance between the two eye centers serves as the principal parameter of measurement for locating the centers of other facial feature regions. Hence, our method works by detecting the two eye centers in every possible situation of eyes and isolating each of the facial feature regions using the proposed anthropometric face model . Combinations of differnt image processing techniques are then applied within the localized regions for detecting the eighteen most important facial feature points. Experimental result shows that the developed system can detect the eighteen feature points successfully in 90.44% cases when applied over the test databases.