Seminar topics for students Face Recognition Grand Challenge - seminar topics,it seminar topics,latest seminar topics,seminar topics on computer science

Face recognition systems

abstract

The primary goal of the FRGC was to promote and advance face recognition technology designed to support existing face recognition efforts in the U.S. Government. FRGC developed new face recognition techniques and prototype systems while increasing performance by an order of magnitude. The FRGC was open to face recognition researchers and developers in companies, academia, and research institutions. FRGC ran from May 2004 to March 2006.

The FRGC consisted of progressively difficult challenge problems. Each challenge problem consisted of a data set of facial images and a defined set of experiments. One of the impediments to developing improved face recognition is the lack of data. The FRGC challenge problems include sufficient data to overcome this impediment. The set of defined experiments assists researchers and developers in making progress on meeting the new performance goals.

There are three main contenders for improving face recognition algorithms: high resolution images, three-dimensional (3D) face recognition, and new preprocessing techniques. The FRGC is simultaneously pursuing and will assess the merit of all three techniques. Current face recognition systems are designed to work on relatively small still facial images. The traditional method for measuring the size of a face is the number of pixels between the centers of the eyes. In current images there are 40 to 60 pixels between the centers of the eyes (10,000 to 20,000 pixels on the face). In the FRGC, high resolution images consist of facial images with 250 pixels between the centers of the eyes on average. The FRGC will facilitate the development of new algorithms that take advantage of the additional information inherent in high resolution images.





Three-dimensional (3D) face recognition algorithms identify faces from the 3D shape of a person's face. In current face recognition systems, changes in lighting (illumination) and pose of the face reduce performance. Because the shape of faces is not affected by changes in lighting or pose, 3D face recognition has the potential to improve performance under these conditions.

In the last couple years there have been advances in computer graphics and computer vision on modeling lighting and pose changes in facial imagery. These advances have led to the development of new computer algorithms that can automatically correct for lighting and pose changes in facial imagery. These new algorithms work by preprocessing a facial image to correct for lighting and pose prior to being processed through a face recognition system. The preprocessing portion of the FRGC will measure the impact of new preprocessing algorithms on recognition performance.

The FRGC improved the capabilities of automatic face recognition systems through experimentation with clearly stated goals and challenge problems. Researchers and developers can develop new algorithms and systems that meet the FRGC goals. The development of the new algorithms and systems is facilitated by the FRGC challenge problems.




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Expert Systems and decision making - seminar topics,it seminar topics,latest seminar topics,seminar topics on computer science

Expert Systems and decision making

Abstract

One of the most successful applications of artificial intelligence reasoning techniques using facts and rules has been in building expert systems that embody knowledge about a specialized field of human endeavour, such as medicine, engineering, or business.
Knowledge Base and the Inference Engine are two major parts of the expert system. The knowledge base consists of facts and rules about the subject at hand. The inference engine consists of all processes that manipulate the knowledge base to deduce information requested by the user.

Knowledge acquisition is a crucial stage in the development of expert systems. As a process, it involves eliciting, interpreting and representing the knowledge from a given domain. Knowledge acquisition for expert systems (from domain experts) is time consuming, expensive and potentially unreliable.

In the construction of an expert system, a ‘knowledge engineer’ (usually a computer scientist with artificial intelligence training) works with an expert (or experts) in the field of application in order. to represent the relevant knowledge of the expert in a form that can be entered into the knowledge base. This process is often aided by a knowledge acquisition subsystem that, among other things, checks the growing knowledge base for possible inconsistencies and incomplete information. These are then presented to the expert for resolution.












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Embedded Auditors for Intrusion Detection - seminar topics,it seminar topics,latest seminar topics,seminar topics on computer science

Embedded Auditors for Intrusion Detection

 

Abstract

A basic cornerstone of security is to verify the integrity of fundamental data stored in the system.

This integrity checking is being achieved using integrity tools such Tripwire, which depend on the integrity and proper operation of the operating system, i.e. these applications assume that the operating system always operates correctly. When this assumption is not valid, the integrity applications cannot provide a reliable result, and consequently may provide a false negative. Once the operating system is compromised, a novice attacker, using tools widely available on the Internet (rootshell.com, etc), could easily defeat integrity tools that rely on the operating system.

A novel way to overcome this traditional integrity problem is to use an independent auditor. The independent auditor uses an out-of-band verification process that does not depend on the underlying operating system. The resultant system provides extremely strong integrity guarantees, detecting modifications to approved objects as well as detecting the existence of unapproved and thus unsigned objects. This is accomplished without any modifications to the host operating system. StrongARM EBSA-285 Evaluation Board, with a SA-110 microprocessor and 21285 core logic can be used as auditors.





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