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IJSTR >> Volume 9 - Issue 1, January 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Composite Sketch Based Face Recognition Using ANN Classification

[Full Text]



Shivaleela Patil, Dr.Shibhangi D C



Forensic Sketch, Composite Sketch, Facial Components, Geometrical Model, Multi-Scale Local Binary Patterns (MLBP), Tchebichef Moment Invariant feature and ANN Classifier.



Today Computer based Technologies have been boosted much procedure and process involved in preparations of crime view documents. In this view point, photography is the first step and important clue to start or to solve investigation of crime, helps in tracing and matching the facial composites against database related to the memory of eyewitness. The facial composites i.e., sketches drawn by the artists or software aids the law enforcement using the description given by the witness in direct to depict the suspects and missing persons, which are posted on public places and helps in recognizing. These methods are found to be useful and many criminals have been recognized through this way. Since the combined sketches provide better and accurate and 80% of law enforcement insists for composite sketches rather than forensic sketch. Therefore in this proposed system, we are focusing on composite sketch based face recognition. First detect the face section using AdaBoost algorithm and detect the facial mechanism using the geometrical model of the face. Features are removed from each individual facial parts by using multi-scale local binary patterns (MLBP) and Tchebichef moment invariant feature. Finally, the ANN classifier is trained to identify the person classified.



[1] Shubhangi A. Wakode and Sunil R. Gupta, “Suspect Identification By Matching Composite Sketch With Mug-Shot”, A &V Publications, Vol. 6, No. 2, pp. 258-262, 2015.
[2] Archana Uphade and J.V. Shinde, “Matching Composite Sketches To Facial Photos Using Component-Based Approach”, International Journal of Computer Applications, Vol. 129, No.15, pp. 17-21, 2015.
[3] Deepinder Singh and Dr. Rajat Gupta, “Matching Facial Composite Sketches to Police Mug-Shot Images Based on Geometric Features”, IOSR Journal of Computer Engineering, Vol. 16, Issue 3, pp. 29-35, 2014.
[4] Paritosh Mittal, Mayank Vatsa, and Richa Singh, “Composite Sketch Recognition via Deep Network - A Transfer Learning Approach”, IEEE, pp. 251 – 256, 2015.
[5] Mr. Steven Lawrence Fernandes and Dr. G Josemin Balab, “Developing a Novel Technique to Match Composite Sketches with Images captured by Unmanned Aerial Vehicle”, ELSEVIER, Vol. 78, pp. 248-254, 2015.
[6] Hu Han, Brendan F. Klare, Kathryn Bonnen and Anil K. Jain, “A Component-Based Approach,” IEEE, Vol. 8, No. 1, pp. 191-204, 2013.
[7] Reshma C. Mohan, Jayamohan and Arya raj, “Matching Sketches with Digital Face Images using MCWLD and Image Moment Invariant”, IOSR Journal of computer engineering, Vol. 17, Issue 6, pp. 131-137, 2015.
[8] Tarang Chugh, Himanshu S. Bhatt, Richa Singh and Mayank Vatsa, “Matching Age Separated Composite Sketches and Digital Face Images”, IEEE, pp 1-6, 2013.
[9] Hongjian Zhang, Ping He, and Xudong Yang, “Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm”, Symmetry, Vol. 7, No. 4, pp. 1734-1750, 2015.
[10] Scott J. Klum, Hu Han, Brendan F. Klare and Anil K. Jain, “The Face Sketch ID System: Matching Facial Composites to MugShots”, IEEE, Vol. 9, Issue 12, pp. 2248 – 2263, 2014.
[11] D. Sridhar and Dr. I. V. Murali Krishna, “Face Recognition using Tchebichef Moments”, International Journal of Information & Network Security, Vol. 1, No. 4, pp. 243-254, 2012.
[12] Ali Nadhim Razzaq, Zahir Hussain and Hind Rustum Mohammed, “Structural Geodesic-Tchebychev Transform: An Image Similarity Measure for Face Recognition”, Journal of Computer Sciences, Vol. 12, No. 9, pp. 464-470, 2016.
[13] Chunlei Peng, Xinbo Gao, Senior Member, Nannan Wang and Jie Li, “Graphical Representation for Heterogeneous Face Recognition”, IEEE, Vol. 39, No. 2, pp 301-312, 2017.
[14] Kumar, Tarun, Kushal Veer Singh and Shekhar Malik, “Artificial Neural Network in Face Detection”, International Journal of Computer Applications, Vol. 14, No. 3, pp. 5-7, 2011.
[15] Ahonen, Timo, Abdenour Hadid and Matti Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition”, IEEE, Vol. 28, No. 12, pp. 2037-2041, 2006.
[16] Ganesh S. Pakmode, “Component-Based Representation Approach to Recognize Face Photo Using Composite Sketches”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 6, pp. 5756-5758, 2015.
[17] Sannidhan M and Dr. Ananth Prabhu G, “A Comprehensive Review on Various State of-The-Art Techniques for Composite Sketch Matching”, IJIR, Vol. 2, No. 12, 2016.
[18] Qianwen Wan and Karen Panetta, “A Facial Recognition System for Matching Computerized Composite Sketches to Facial Photos using Human Visual System Algorithms”, IEEE, pp. 1-6, 2016.
[19] Mittal Paritosh, Mayank Vatsa, and Richa Singh, “Composite Sketch Recognition via Deep Network-A Transfer Learning Approach,” IEEE, pp. 251-2562015, 2015.
[20] Klum Scott, Hu Han, Anil K, and Brendan Klare. “Sketch-Based Face Recognition: Forensic vs. Composite Sketches”, IEEE, pp. 1-8, 2013.
[21] Scott Klum, Hu Han, Brendan Klare, and Anil K. Jain, “The FaceSketchID System: Matching Facial Composites to Mugshots.”, IEEE Transaction on Information Forensics and Security (TIFS), Vol. 9, No. 12, pp. 2248-2263, Dec. 2014.
[22] FACES: http://www.facesid.com/
[23] Identi-Kit: http://www.identikit.net/