Face Recognition Using Fixed Spread Radial Basis Function Neural Network

This paper presents face recognition using spread fixed spread radial basis function neural network. Acquired image will be going through image processing process. General preprocessing approach is use for normalizing the image. Radial Basis Function Neural Network is use for face recognition and Support Vector Machine is used as the face detector. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF but in this paper fixed spread is used as there is only one train image for every user and to limit the output value

Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value

This paper present a face detection system using Radial Basis Function Neural Networks With Fixed Spread Value. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and a Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face images. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. In this paper, a uniform fixed spread value will be used. The performance of the RBFNN face detection system will be based on the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) criteria. In this research, the best setting for RBF face detection were summarized into one table where by using center 200 and spread 4 gives the highest detection rate and the lowest FAR as well as FRR. But for detecting many faces in a single image, center 200 and spread 5 is the best setting as the system can detect all faces in the image

The Effect of Overlapping Spread Value for Radial Basis Function Neural Network in Face Detection

In this paper, the effect of overlapping spread value for Radial Basis Function Neural Network (RBFNN) in face detection is presented. The reason for taking the overlapping factor into consideration is to optimize the results for using variance spread value. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and a Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face images. RBFNN offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. The performance of the RBFNN face detection system will be based on the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) criteri

Stocking and species composition of second growth forests in Peninsular Malaysia.

Management of the production forests in Malaysia is currently undergoing a major change as the total extent of undisturbed forest being harvested is diminishing. Currently most of the harvesting operations are being conducted in second growth (rotation) forests and in the near future all production forest will solely consist of only logged forests. This is expected to result in a significant reduction of the supply of raw materials to the industry because second growth forest stands are generally poorer and not so well-stocked with quality timber species. According to the forest management systems applied to these forests, namely the Selective Management System (SMS) and the Malayan Uniform System (MUS), the residual forests should be able to recover in the specified rotation cycle and there should be sufficient quality crop for the second and subsequent harvests. To understand the situation, a study was carried to assess the stocking and species composition of second rotation forests in two production forests located in Tekam Forests Reserve, Pahang and Cherul Forest Reserve, Terengganu. The study results indicated that the second rotation forests are not as productive as predicted but still able to produce an economic harvest in terms of total timber yield within the specified rotation cycle. However, based on inventory projections of existing stocks, it was found that in general the forests have not fully recovered in terms of stocking of commercial species. Species composition has been altered favouring higher dominance of non-dipterocarp species. Some of the major factors that could have contributed to this phenomenon are slower recovery of the forest after the first cut, higher mortality due to logging damage, and implementation of cutting limit prescriptions that favour high removal of dipterocarps as they are dominant in the upper diameter classes. It must be noted that the second growth forest assessed were those that were more than 20 years old. Currently, forest management practices have improved significantly and thus the recent second growth forests are expected to be in a much better condition. The information generated from this project on the status of the stocking and species composition of second growth forest will be essential for improving planning and management of the resource with the aim of enhancing future productivity

Vertical motion controller design of an underwater vehicle

This paper presents the performance of different control approaches that have been employed in controlling the vertical motion of an autonomous underwater vehicle (AUV). Different control schemes, based on conventional proportional derivative (PD) controller and an intelligent controller such as fuzzy logic (FL) controller techniques are proposed and their performance is compared. At the end of this study, the intelligent controller shows better result in term of rise time, where the conventional controller value is approximately 39.49 seconds and the intelligent controller is 35.39 seconds, respectively