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|Title:||What can the structure of the palm print tell us?||Authors:||Sont Rahardja.||Keywords:||DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision||Issue Date:||2012||Abstract:||Biometric based palm print identification technique has been attracting a lot of attention in recent years and it has always been an area of interest for development. This is because palm print can be used to easily distinguish different people and it will remain unchanged unlike other body parts like the iris. The palm print is also harder to replicate compared to the finger print. Other than identification purposes, palm print is also traditionally used to predict the fortune of an individual. This fortune telling technique is commonly known as palm reading or palmistry, and has also been implemented into a fortune telling system. The general idea of the fortune telling system is to scan the palm, extract the region of interest and compare it to the database in order to get the closest set of data. In this case, the region of interest will be the 3 principle lines which consist of the heart line, head line and life line. The extraction of the 3 principle lines from a scanned palm image proves to be a challenge itself because there are many different lines intersecting one another. Therefore we must find a solution to identify the 3 principle lines as accurately as possible. In this report, we will focus mainly on the identification of the 3 principle lines and matching it to the set of database. When a raw palm print is received, there are many different lines but not all the lines on the palm print are useful for the fortune telling system. Therefore we must attempt to identify the 3 principle lines and eliminate all other redundant lines that are found in the palm print. This is done in mainly 2 phases which is feature extraction and palm print identification. In feature extraction we will be identifying the region of interest and enhancing the input image using methods such as edge detection and feature detection. In palm print identification, we will be using the Hough Transform method, which will be explained in details later in the report.||URI:||http://hdl.handle.net/10356/48849||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
Updated on Dec 5, 2020
Updated on Dec 5, 2020
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