Rokaia Shalal Habeeb

Work place: Mustansiriyah University/College of Engineering, Computer Engineering Department, Baghdad, Iraq



Research Interests: Computer systems and computational processes, Image Compression, Image Manipulation, Image Processing, Data Structures and Algorithms, Analysis of Algorithms, Combinatorial Optimization


Rokaia Shalal Habeeb is a lecturer at the computer engineering department, College of Engineering, Mustansiriyah University, Baghdad, Iraq since 1997. She received her B.Sc. in Electrical Engineering from Mustansiriyah University/Baghdad, Iraq in 1995 and her M.Sc. degree in Electronic and Communication Engineering from Mustansiriyah University/Baghdad in 2002. Her research interests include Intelligent Systems, Optimization Algorithms, and Image Processing.

Author Articles
Analysis of Large Set of Images Using MapReduce Framework

By Sawsan M. Mahmoud Rokaia Shalal Habeeb

DOI:, Pub. Date: 8 Dec. 2019

Due to the limitations of a physical memory, it is quite difficult to analyze and process big datasets. The Hadoop MapReduce algorithm has been widely used to process and mine such large sets of data using the Map and Reduce functions. The main contribution of this paper is to implement MapReduce programming algorithm to analyze large set of fingerprint images which cannot be normally processed due to a limited physical memory in order to find the features of these images at once. At first, the images are maintained in an image data store in order to be preprocessed and to extract the features for the biometric trait of each user, and then store them in a database. The algorithm preprocesses and extracts the features (ridges and bifurcation) from multiple fingerprint images at the same time. The extracted points are detected using the Crossing Number (CN) concept based on the proposed algorithm. It is validated using data taken from the National Institute of Standards and Technology’s (NIST) Special Database 4. The data consist of fingerprint images for many users. Our experiments on these large set of fingerprint images shows a significant reducing in the processing time to a nearly half when extracting the features of these images using our proposed MapReduce approach.

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