The Analysis On The System

Biometrics technology aims at utilizing major and distinctive characteristics such as behavioral or biological, for the sake of positively indentifying people. With the help of a combination of hardware and specific identifying sets of rules, a basic human attribute, automated biometric recognition mimics to distinguish and categorize other people as individual and unique. But the challenges surrounding biometrics are great as well.

In the present contemporary era, facial recognition technologies are being installed by the companies in an extensive sense that surely reflects a continuum of growing hi-tech superiority and complexity. At the most ordinary level, facial detection is done by this technology which means that a photo is just detected and located for a face ("Facing Facts: Best Practices for Common Uses of Facial Recognition Technologies," 2012).

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

The basic principle of this algorithm is to recognize the input paper currency. First of all acquired the image from a particular source. As in this thesis we use for reference images. System read the particular image. Then resize the image. After that the color separator convert the image into RGB to Gray scale and then in binary image. After that the system use color noise median filter. The currency length detector detects the length of the currency. Using the feature extraction techniques the system detect the particular feature of that currency and then the system use pattern matching algorithm to math that particular feature. The input image match with particular database image and according to that we find the currency. In this way this thesis design a automatic system in which we can recognized the paper currency.

Iain’s & Co. are a large building company that currently employ a team of IT technicians to help them manage their IT infrastructure and their IT users. They recently have taken over a similar small company that employs technical staff in the same way. Iain’s & Co. feel that they need a centralised IT support system that will monitor, track and report problems that have been identified by their IT users across both sites. Iain’s & Co. have hired me as their Systems Analyst to provide them better solutions to manage their IT Supports system.

Systems theory: a scientific/philosophical approach and set of concepts, rather than a theory, for the transdisciplinary study of complex phenomena. It was first proposed by the biologist Ludwig von Bertalanffy in the 1940's (anthology: "General Systems Theory", 1968), as a reaction against scientific reductionism*. Rather than reducing a phenomenon (say, the human body) to a collection of elements or parts (say, the organs or cells), systems theory focuses on the relations and interactions between the parts, which connect them into a whole (see holism*). The particular arrangement of

Through this routine of advanced technology analysis, it has been established to increase the results and have hastened the procedure of identifying suspects of crimes. Facial recognition is also necessary for public involvement and observation as it also aids law enforcement officials to more easily zone in on possible suspects of a crimes being caught. With the use of facial recognition, it constantly has been proven quite an effective method with the incorporation of this technique.

important for systems and is a type of input. A system can have either positive feedback

Traditionally, the biometric based technologies perform the identification of the person based on the physiological characters

In this way the biometric data of the user through sensors pick up and then passed to the feature extractor to generate a template.

For more than thirty years, reaserchers have been working on handwritten recognition. Over the past few years, the number of companies involved in research on handwritten recognition has continually increased. The advance of handwritten processing results from a combination of various elements,

Iris recognition has become the most reliable method of automatic identification. The growing use of this biometric method is based on the visible complex structure of the iris. Traditional methods of automatic identification rely on special possessions such as cards and passports or secrets like passwords and PIN numbers. The disadvantage of these methods is that they can be separated from the person. Examples of biometrics include iris, retina, voice patterns, face recognition, fingerprints as these methods use something that is very complex in their anatomy, something visual about them or can be obtained in real time. Efficient recognition systems can be built because of the variability amongst every individual’s iris pattern. This paper explains the iris recognition process with the implementation of Daugman’s original algorithms.

To extract the small and large images by using the method of detecting edges of some predefined range of orientation in order to remove the problem related to detecting such boundaries.

In terms of recognizing a person, there are several algorithms using digital image processing of the iris have been proposed. More precisely after acquisition phase, a specific signature is encoded for biometric pattern that can be used for authentication and identification purposes. The Figure - 2 shows the general diagram of an iris recognition system.

For this procedure, a recent algorithm was developed by Otsu which is called as Otsu’s algorithm. This algorithm can find the exact location of the connected pixels with same properties and the borders of the regions. The main parameters that are to be considered in this function are ratio of component area, aspect ratio, extent, component area of the border area to the plane area. The measurement of these factors are very crucial for the subsequent recognition of the elements.