Fusion for Component based Face Recognition

This paper proposes a practical way to realize the diversity in face recognition system for performance improvement by fusing the classification results from the components (characteristic regions such as eyes, nose and mouth) and from the whole face image, instead of concatenating the face feature and the modular features for a single classifier. The extracted sub-images are not totally independent from the face image, but the experiments show that the fused result is improved from the recognition result based on the face or components alone. The fusion is implemented and compared at both score level and decision level. Communication resources are preserved between the sensor and fusion point in decision level fusion at the expense of performance, and the selection of which fusion scheme to use depends on the system resources and performance requirement

A Predictive Sensor Network Using Ant System

The need for a robust predictive sensor communication network inspired this research. There are many critical issues in a communication network with different data rate requirements, limited power and bandwidth. Energy consumption is one of the key issues in a sensor network as energy dissipation occurs during routing, communication and monitoring of the environment. This paper covers the routing of a sensor communication network by applying an evolutionary algorithm- the ant system. The issues considered include optimal energy, data fusion from different sensor types and predicting changes in environment with respect to time

A Predictive Sensor Network Using Ant System

The need for a robust predictive sensor communication network inspired this research. There are many critical issues in a communication network with different data rate requirements, limited power and bandwidth. Energy consumption is one of the key issues in a sensor network as energy dissipation occurs during routing, communication and monitoring of the environment. This paper covers the routing of a sensor communication network by applying an evolutionary algorithm- the ant system. The issues considered include optimal energy, data fusion from different sensor types and predicting changes in environment with respect to time

Balancing the performance of a sensor network using an ant system

Abstract- In a sensor network consisting of both wired and wireless links, the nodes sense, collect and distribute dynamic information from one sensor to the other. Energy consumption is a key issue in the sensor’s communications since many use battery power, which is limited. The sensors also have limited memory and functionality to support communications. Therefore, there is a need to balance energy usage with obtaining the shortest communication distance. This paper presents a novel approach to selecting message routes using an ant system. Parameters controlling the convergence of the ant system are analyzed in terms of wired and wireless networks. I

Distributed Wireless Face Recognition System

A face recognition system gains flexibility and cost efficiency while being integrated into a wireless network. Meanwhile, face recognition enhances the functionality and security of the wireless network. This paper proposes a distributed wireless network prototype, consisting of feature net and database net, to accomplish face identification task by optimally allocating network resources. The face recognition technique used in this paper is subspace-based modular processing with score and decision level fusion. The subspace features are selected by a step-wise statistical procedure, Modified Indifference-Zone Method, which improves efficiency and accuracy. Fusion further improves the performance from using either the whole face or modules alone. The face recognition techniques are re-engineered to be implemented on the distributed wireless network, and the simulation result shows promising improvement over centralized recognition

Sensor Management by using Bayesian Networks

Abstract- This paper introduces the sensor management problem and uses Bayesian networks as a scalable approach to handling the operational decisions concerning the sensor network. In general, single sensor systems only provide partial information on the state of the event or environment while multisensor systems provide a synergistic effect, which improves the quality and availability of information. Data fusion techniques can effectively combine this environmental information from similar and/or dissimilar sensors. Until recently, the operator could manage these multiple systems easily, but current systems are more complex and produce data more quickly than earlier versions. A sensor manager becomes necessary when this occurs to assist the operators. Researchers have developed many single point sensor management solutions. I

502 Intra-difference based Segmentation and Face Identification

This paper utilizes the intra-difference in still images to segment a face from its background and then combines the intra-difference detection result with the eigenface/eigenfeature methods to identify the face. This novel diverse scheme can finally solve the problem of accuracy in practical applications, thus broadening the application of face recognition into more versatile situations such as security building entrance, customs and mug spotting. The organic combination of intra-difference detection method and eigenface/eigenfeature methods into one system is shown to be more robust and have a better identification rate than either method alone. This paper first addresses the problems of the real-time accuracy issue and the need of pre-processing (mainly normalization). And then it proposes to use intra-difference to effectively segment a human face. The segmented face is further processed by both intra-difference detection method and eigenface/eigenfeature methods to determine its identity. Correspondingly, the proposed algorithm consists of three parts: segmentation, pre-processing, and multi-phase face identification by fusing the results from both the intra-difference detection method and the eigenface/eigenfeature methods

Dynamic Sensor Management Using Multi Objective Particle Swarm Optimizer

This paper presents a Swarm Intelligence based approach for sensor management of a multi sensor networks. Alternate sensor configurations and fusion strategies are evaluated by swarm agents, and an optimum configuration and fusion strategy evolves. An evolutionary algorithm, particle swarm optimization, is modified to optimize two objectives: accuracy and time. The output of the algorithm is the choice of sensors, individual sensor's thresholds and the optimal decision fusion rule. The results achieved show the capability of the algorithm in selecting optimal configuration for a given requirement consisting of multiple objectives