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  Eyes Detection by Pulse Coupled Neural Networks  
  Authors : Maminiaina Alphonse Rafidison ; Andry Auguste Randriamitantsoa ; Paul Auguste Randriamitantsoa
  Cite as: ijcsn.org/IJCSN-2013/2-5/IJCSN-2013-2-5-21.pdf


This paper presents a new method fast and robust for eyes detection, using Pulse-Coupled Neural Networks (PCNN). The functionality is not the same as traditional neural network because there are no training steps. Due of this feature, the algorithm response time is around tree millisecond. The approach has two components including: face area detection based on segmentation and eyes detection using edge. The both operations are ensured by PCNN The biggest region which is constituted by pixel value one will be the human face area. The segmented face zone which will be the input of PCNN for edge detection undergoes a vertical gradient operation. The two gravity’s center of close edge near the horizontal line which corresponds to the peak value of horizontal projection of vertical gradient image will be the eyes.


Published In : IJCSN Journal Volume 2, Issue 5

Date of Publication : 01 October 2013

Pages : 11 - 18

Figures : 21

Tables : 02

Publication Link : ijcsn.org/IJCSN-2013/2-5/IJCSN-2013-2-5-21.pdf




Maminiaina A. Rafidison : was born in Moramanga, Madagascar on 1984. He received his Engineer Diploma in Telecommunication on 2007 and M.Sc. on 2011 at High School Polytechnic of Antananarivo, Madagascar. Currently, he is a consultant expert on Value Added Service (VAS) in telecom domain at Mahindra Comviva Technologies and in parallel; he is a Ph.D. student at High School Polytechnic of Antananarivo. His current research is regarding image processing especially using Neural Networks.

Andry A. Randriamitantsoa : received his Engineer Diploma in Telecommunication on 2008 at High School Polytechnic of Antananarivo, Madagascar and his M.Sc. on 2009. Currently he is working for High School Polytechnic and he had a PhD in Automatic and Computer Science in 2013. His research interests include Automatic, robust command, computer science.

Paul A. Randriamitantsoa : was born in Madagascar on 1953. He is a professor at High School Polytechnic of Antananarivo and first responsible of Telecommunication- Automatic – Signal – Image Research Laboratory.








Pulse Coupled Neural Networks

Face Detection

Eyes Detection

Image Segmentation

Edge Detection






In this paper, we proposed a method for eyes detection using Pulse Coupled Neural Networks PCNN which is inspired by the human visual cortex. The algorithm has a two parts: face detection which is based on segmentation and eyes detection based on edge detection. The method is very fast due of iteration instead of image database learning. The time requirement of the algorithm is three millisecond which is acceptable for real time applications and less than this for grayscale image. The success rate is up to 99.4% for a picture with a person without glasses against 97.6% with glasses.










[1] F. D. Frate, G. Licciardi, F. Pacifici, C. Pratola, and D. Solimini, "Pulse Coupled Neural Network for Automatic Features Extraction from Cosmo-Skymed and Terrasar-x imagery", Tor Vergata Earth Observation Lab – Dipartimento di Informatica, Sistemi e Produzione, Tor Vergata University, Via del Politecnico 1, 00133 Rome, Italy, 2009.

[2] A. Soetedjo, "Eye Detection Based-on Color and Shape Features", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 5, 2011, pp. 17-22, 2011.

[3] T. Lindblad, J. M. Kinser, "Image processing Using Pulse-Coupled Neural Networks", Second, Revised Edition, Springer, 2005.

[4] I. Choi, D. Kim, "Eye correction using correlation information", In Y. Yagi et al. (Eds.): ACCV 2007, Part I, LNCS 4843, pp. 698-707, 2007.

[5] Z. Zhou, X. Geng, "Projection functions for eye detection, Pattern Recognition", Vol. 37, pp. 1049-1056, 2004.

[6] J. Song, Z. Chi, J. Liu, "A robust eye detection method using combined binary edge and intensity information, Pattern Recognition", Vol. 39, pp. 1110-1125, 2006.

[7] T. Kawaguchi, M. Rizon, "Iris detection using intensity and edge information, Pattern Recognition", Vol. 36, pp. 549-562, 2003.

[8] S. Asteriadis, N. Nikolaidis, A. Hajdu, I. Pitas, "An Eye Detection Algorithm Using Pixel to Edge Information", Department of Informatics, Aristotle University of Thessaloniki, Box 451, 54124, Thessaloniki, Greece, 2010.