Abstract-In this paper, we have proposed probabilistic parser for identifying the face in a given scene image. Many object detection techniques use pattern statistical methods for feature extraction which is resource intensive and time consuming. We proposed a novel certainty factor based geometrical formulation for facial feature extraction. The proposed method accurately detects the facial components like eyes, nose and mouth in the presence of complex background. In the next stage, the AND/OR graph based recursive Top-down/Bottom-up image parser is used to detect the face in the input image by using the detected facial components. The image parser grammar represents both the decomposition of the scene image and the context for spatial relation by horizontal link between the vertices of the graph. The AND/OR graph is used to represent compositional structure of the image. The AND node represents the decomposition of the visual object into number of components and OR node represents the alternative sub-configuration /component. The experimental result confirms that our method outperforms some of the existing face detection methods.

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This paper presents a new method for feature extraction from the facial image by using bunch graph method. These extracted geometric features of the face are used subsequently for face recognition by utilizing the group based adaptive neural network. This method is suitable, when the facial images are rotation and translation invariant. Further the technique also free from size invariance of facial image and is capable of identifying the facial images correctly when corrupted with noise/ camouflage. The method shows a result of 97% correct result in facial database of 320 images of 10 classes. The technique can be extended easily to incorporate the rotational invariance of the images.

& The paper discusses the concept of re-planning for a mobile robot in the presence of semidynamic obstacles. The navigational planning is done by employing genetic algorithm until it reaches the goal point. The path segments traversed by the mobile robot are stored by a simple matrix, employing temporal associative memory. During subsequent traversal, the robot utilizes the previously stored matrix to avoid an obstacle path. In case of deadlock, the robot back tracks using TAM and finds alternative paths to reach the goal. This algorithm has been realized on a Pioneer 2DX mobile robot of ActiveMedia Robotic LLC, USA, through client server architecture. The result shows that the robot reaches the goal within a vicinity of a 20 mm radius.

Abstract:The major issues in the business intelligence is to predict the customer behavior and understanding them for the betterment of the business. This understanding and behavior are to be analyzed on the basis of the information given by the customer. It is important to determine customer preferences in formulating market strategies that is taken from the concept of Business Intelligence (BI) and analysis. This analysis is based on the customer profile. For these kinds of analysis, a large number of datasets are required. Having analyzed the data set in the paper [1], it was realized that more number of dataset will give maximum precession. To get these kinds of data set formulated for the work, is very difficult. This is the reason, the work has been concluded to go for genetic algorithm to generate new groups of datasets to augment the existing one, with a proper validation. Also, it has been decided to give more priority for the correlation by effectively using multiple correlation, to get an appropriate result in this work.