Face recognition using Partitioned Iterated Function System

Biometrics has been used for person identification since long time. Till today, researchers, neuroscientists and engineers working on the how human brain works for identification of the person. Hence it is a very interesting topic to do the work on it. Every individual has unique fingerprint and it does not changes throughout the lifetime. So it is the most widely used technique. But the face is most natural, non intrusive biometric and the cost of the system is low as compared with the other biometric system. Many conventional methods of face recognition depend solely on appearance and model based. But there is inbuilt degree of self similarity in the image of faces which can be efficiently utilized through representation exploiting self-transformations, known as Iterated Function System (IFS). Interestingly, virtually all images of natural or man-made objects, show region wise self similarity although they may not be globally self similar. Such objects can be represented by Partitioned Iterated Function System (PIFS) very compactly. Hence, the goal is to carry out the face recognition using Partitioned Iterated Function System. This approach has been tested upon the 106 images of 27 persons. It is giving the good results under the variance, rotation and scaling. In this approach we carried out face recognition based on PIFS representation and matching carried out in the PIFS code domain, which is more efficient than correlation in the image domain. The recognition method is attractive from the view point of execution time as the PIFS code of reference faces are built off-line and recognition of query object involves only comparison of its PIFS code with those in the database.