Recognizing Expression Variant and Occluded Face Images Based on Nested HMM and Fuzzy Rule Based Approach

The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.

A Model of Cellular Automata for the Fuzzy Control of Aphids

Pesticides are substances used to prevent, destroy or mitigate any pest. We have adopted in this paper the Cellular Automata model to study the dispersion of the aphids in the block of citric trees using the pesticides (chemical control) and the biological agent (biological control). The main purpose of this research is the development of a simple and specific methodology to study Citrus Sudden Death (CSD). CSD is a disease that has affected sweet orange trees grafted on Rangpur lime in the state of S?o Paulo-Brazil. Some studies suggest that this disease has been caused by a virus and it is transmitted by insects known as aphids (vector). The ladybug was selected among the most known enemies of aphids in citrus in Brazil. In order to elaborate a predator-prey type of model to study the interaction between aphids (preys) and ladybugs (predators) in citriculture we have used a fuzzy rule-based system (FRBS). The states of the variables of the system (inputs) are the density of preys and the density of predators and their variations are the outputs. Therefore we take into account the effect of the wind in the space covered by the aphid, since the wind is important for the flight of the aphid as described in Peixoto et al. (2008) [1]. After, we used a FRBS to establish the relationship between the quantity of pesticides and the density of the preys. The simulations have been performed and have been compared between blocks with the presence of both aphids and ladybugs without the use of pesticides and the presence of them with the use of these ones using the Cellular Automata model. Numerical simulations allow us to foresee the behavior of the system, hence creating a spectrum of possibilities and proposing control techniques for different initial scenarios.

Hybrid approach for fuzzy system design

A hybrid approach for fuzzy system design based on clustering and a kind of neurofuzzy networks is proposed. An unsupervised clustering technique is firstly used to determine the number of if-then fuzzy rules and generate an initial fuzzy rule base from the given input-output data. Then, a class of neurofuzzy networks is constructed and its weights are tuned so that the obtained fuzzy rule base has a high accuracy. Finally, two examples of function approximation problems are given to illustrate the effectiveness of the proposed approach.

Integral Performance Criteria Based Analysis of Load Frequency Control in Bilateral Based Market

Performance index based analysis is made to examine and highlight the effective application of Particle Swarm Optimization (PSO) to optimize the Proportional Integral gains for Load Frequency Control (LFC) in a restructured power system that operates under Bilateral based policy scheme. Various Integral Performance Criteria measures are taken as fitness function in PSO and are compared using overshoot, settling time and frequency and tie-line power deviation following a step load perturbation (SLP). The motivation for using different fitness technique in PSO is to show the behavior of the controller for a wide range of system parameters and load changes. Error based analysis with parametric uncertainties and load changes are tested on a two-area restructured power system. The results of the proposed PSO based controller show the better performance compared to the classical Ziegler-Nichols (Z-N) tuned PI and Fuzzy Rule based PI controller.