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.
IJCSN International Journal of Computer Science and Network, Volume 2, Issue 5, October 2013 ISSN (Online) : 2277-5420 www.ijcsn.org 11 Eyes Detection by Pulse Coupled Neural Networks 1 Maminiaina Alphonse Rafidison, 2Andry Auguste Randriamitantsoa, 3Paul Auguste Randriamitantsoa 1, 2, 3 Telecommunication- Automatic – Signal – Image Research Laboratory High School Polytechnic of Antananarivo, University of Antananarivo Antananarivo, Ankatso BP 1500, Madagascar Abstract This paper presents a new method fast and robust for eyes nodes coupled together with their neighbors within a detection, using Pulse-Coupled Neural Networks (PCNN). The definite distance, forming a grid (2Dvector). The PCNN functionality is not the same as traditional neural network neuron has two input compartments: linking and feeding. because there are no training steps. Due of this feature, the The feeding compartment receives both an external and a algorithm response time is around tree millisecond. The approach has two components including: face area detection local stimulus, whereas the linking compartment only based on segmentation and eyes detection using edge. The both receives a local stimulus. When the internal activity operations are ensured by PCNN The biggest region which is becomes larger than an internal threshold, the neuron constituted by pixel value one will be the human face area. The fires and the threshold sharply increases. Afterward, it segmented face zone which will be the input of PCNN for edge begins to decay until once again the internal activity detection undergoes a vertical gradient operation. The two becomes larger. 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. Keywords: Pulse Coupled Neural networks, Face detection, Eyes detection, Image Segmentation, Edge Detection. 1. Introduction In recent decades, image processing domain has an exponential evolution. The current status is completely different from initial state. Actually, image processing searches are oriented to object recognition especially for face recognition. Eyes detection is an important phase ensuring a good performance of face recognition. In this Fig. 1 Pulse Coupled Neural Networks Structure paper, an eyes detection method is proposed. The method is based on pulse coupled neural networks. It is divided in This process gives rise to the pulsing nature of PCNN, two parts: face detection first, following by eyes detection. forming a wave signature which is invariant to rotation, We will see in the next paragraph the neural network scale and shift or skew of an object within the image. This coupled pulse purpose, then the details of the proposed last feature makes PCNN a suitable approach for feature algorithm followed by the test phase, its performance extraction in very-high resolution imagery, where the measurement and its prospect. view angle of the sensor may play an important role. PCNN system can be defined by the following expression: 2. Pulse Coupled Neural Networks Model (1) The architecture of a Pulse Coupled Neural Networks (PCNN) is rather simpler than most other neural network (2) implementations. PCNN do not have multiple layers and receive input directly from the original image, forming a Where is the input stimulus to the neuron , resulting “pulse” image. The network consists of multiple and are respectively the values of the Feeding and Linking compartment. Each of these neurons IJCSN International Journal of Computer Science and Network, Volume 2, Issue 5, October 2013 ISSN (Online) : 2277-5420 www.ijcsn.org 12 communicates with neighboring neurons by means 3.1 Face Detection of the weights given by M and W kernels. Y is the output of a neuron from the previous iteration, while and Searching face area focuses on the skin detection because indicate normalizing constants. The output of feeding and it is the dominant part in the top portion of human image. linking compartment are combined to create the internal Once we get grayscale image as input, we proceed to state of the neuron : configure the PCNN using the below parameters: (3) Weights matrix A dynamic threshold , is also calculated as follow: (6) (4) In the end, the internal activity is compared with to Initial values of matrix : produce the output , by: The initial values of linking L, feeding F matrix and (5) stimulus S are the same as the input image. The convolution between null matrix which has the same size as the input image RxC and weights matrix initiates the output value Y of PCNN. The first value of dynamic The result of a PCNN processing depends on many parameters. For instance, the linking strength, β, affects threshold is an R-by-C matrix of two. segmentation and, together with M and W, scales feeding and linking inputs, while the normalizing constants scale Delay constants: the internal signals. Moreover, the dimension of the , and convolution kernel affects the propagation speed of the autowave. With the aim of developing an edge detecting , Normalizing constants: PCNN, many tests have been made changing each , , and parameter . The PCNN is ready for iteration exercise. For skin segmentation, the iteration set is n= . We have 3. Proposed Method already gotten an image segmented during the first iteration but to obtain a good result, we repeat the The method doesn’t depend on image input format. In operation three times. The Fig. 4, Fig. 5, Fig 6 show the case of image color, the conversion to grayscale type is PCNN outputs. required. We have two steps to follow: face detection, then eyes detection. The following figure presents shortly the chart of our algorithm. Fig. 2 Face/Eyes detection method Fig. 3 Original image IJCSN International Journal of Computer Science and Network, Volume 2, Issue 5, October 2013 ISSN (Online) : 2277-5420 www.ijcsn.org 13 Fig. 4 PCNN output for first iteration n=1 Fig. 6 PCNN output for third iteration n=3 Once the original image with RxC size is segmented, we calculate the sum of pixel value per row and per column . (7) (8) Projection vertical of PCNN image output 30 25 20 Sum of pixel value 15 Fig. 5 PCNN output for second iteration n=2 10 5 0 0 50 100 150 200 250 Image column Fig. 7 Vertical projection of Fig. 6 IJCSN International Journal of Computer Science and Network, Volume 2, Issue 5, October 2013 ISSN (Online) : 2277-5420 www.ijcsn.org 14 The vertical projection graph presents some peaks values in the column range , same case for horizontal projections in row range Projection Horizontal of PCNN image output 25 20 Sum of pixel value 15 10 5 0 Fig. 10 Face area 0 50 100 150 200 250 300 Image row Fig. 8 Horizontal projection of Fig. 6 3.2 Eyes detection Face area is the intersection region of the two bands; it After detecting the face, the next step is to localize the means the rectangle’s area described on Fig. 9. iris. We need to extract first the content of rectangle from the image segmented PCNN’s output (Fig. 6). Then, we customize each region to be delimited as well. The (9) operation doing this task is available with Matlab called “imclose”. The Fig. 11 presents the result of this operation. Fig. 9 Face detection method Fig. 11 Region customization With our experimental image: The image with region closed becomes the input of PCNN for edge detection. The Pulse Coupled Neural Networks will use the same parameters as before during segmentation steps. Three iterations are enough to get a And we get the following picture: good result of edge detection and the below figures show the output for each iteration. IJCSN International Journal of Computer Science and Network, Volume 2, Issue 5, October 2013 ISSN (Online) : 2277-5420 www.ijcsn.org 15 _ = Fig. 15 Eyes regions candidates Eye regions candidates are found, then we are looking for gravity’s center of each region. For a 2D continuous domain, knowing the characteristic function of a region, Fig. 12 First iteration the raw moment of order ( is defined as : (10) For adapting this to scalar (greyscale) image with pixel intensities , raw image moments are calculated by: (11) The two raw moments order one et , associated with moment order zero are used to calculate the centroid of each region. Its position is defined as: et (12) Fig. 13 Second iteration Now, our problem is « how to identify the eyes? ». To answer this question, we proceed to calculate vertical gradient of face area segmented image. (13) Fig. 14 Third iteration The PCNN has played two important roles: segmentation and edge detection. The closed edge will be filled with Fig. 16 Vertical gradient of face area segmented blank color (“imfill” Matlab function) and we calculate the difference between this one with the image of the last We use the same principle as the face detection by iteration of the PCNN. calculating the sum of gray level of vertical gradient IJCSN International Journal of Computer Science and Network, Volume 2, Issue 5, October 2013 ISSN (Online) : 2277-5420 www.ijcsn.org 16 image per row . We get the peaks and draw the line horizontal corresponding. Projection verticale gradient Y 3000 2000 Sum of vertical gradient projection 1000 0 -1000 -2000 -3000 0 20 40 60 80 100 120 140 160 180 200 Image row Fig. 19 Eyes detected Fig. 17 Horizontal projection of Fig. 16 4. Results and Performance (14) All tests were performed with image color with different dimension. As we know, the algorithm doesn’t use a is the line carrier relevant information in top part of database image for training, so the eyes detection is very image and the two centers of gravity of a region near the fast. However, it has a weakness when the person wears horizontal line are the eyes. The distance between and glasses because the iris is not detected correctly. Samples the center of gravity is calculated by: of the experimental results are shown in the series of pictures (Fig. 20 and Fig. 21) below: (15) Fig. 20 Multiple detection Fig. 18 Line and gravity’s centers positions Finally, the eyes are detected with more precision. IJCSN International Journal of Computer Science and Network, Volume 2, Issue 5, October 2013 ISSN (Online) : 2277-5420 www.ijcsn.org 17 Table 2: Comparison results No. Eyes detection Methods rate success Choi and Kim  1 98.7 % 2 Proposed Method 98.5% S. Asteriadis, N. Nikolaidis, 3 98.2% A. Hajdu, I. Pitas  Song and Liu  4 97.6 % Kawaguchi and Rison  96.8 % 5 Eye detection based-on 96.5% 6 Haarcascade classifier Zhou and Geng  95.9 % 7 5. Conclusion 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. Fig. 21 Testing results Our prospects are turning to extract face feature such as An approximate measure of performance was done by nose and mouth. and are the iris passing image database test used by the methods listed on Table 2 and some image from internet, as input of our position. which is perpendicular line with algorithm. The following table (Table 1) shows the result segment, passes in middle of the both iris. The first black of testing: region from PCNN output (Fig. 6) passed by is the noise and the mouth is the second one. Table 1: Performance measurement Acknowledgments Face Detection Eyes Detection With Authors thank Mrs Hellen Ndiri Ayuku and Mr Arnou glasses 99.6% 99.4% Georges Philippe Jean for English language review. Without 98.4% 97.6% glasses References Total 99% 98.5%  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 With performance 98.5%, we can say that our method is imagery", Tor Vergata Earth Observation Lab – powerful. A comparison with another algorithm was done Dipartimento di Informatica, Sistemi e Produzione, Tor Vergata University, Via del Politecnico 1, 00133 Rome, and the table (Table 2) indicates the results. We can use Italy, 2009. this algorithm for face recognition or reading facial  A. Soetedjo, "Eye Detection Based-on Color and Shape expression. Features", (IJACSA) International Journal of Advanced IJCSN International Journal of Computer Science and Network, Volume 2, Issue 5, October 2013 ISSN (Online) : 2277-5420 www.ijcsn.org 18 Computer Science and Applications, Vol. 3, No. 5, 2011, Maminiaina A. Rafidison was born in Moramanga, Madagascar on pp. 17-22, 2011. 1984. He received his Engineer Diploma in Telecommunication on  T. Lindblad, J. M. Kinser, "Image processing Using 2007 and M.Sc. on 2011 at High School Polytechnic of Antananarivo, Madagascar. Currently, he is a consultant expert on Value Added Pulse-Coupled Neural Networks", Second, Revised Service (VAS) in telecom domain at Mahindra Comviva Technologies Edition, Springer, 2005. and in parallel; he is a Ph.D. student at High School Polytechnic of  I. Choi, D. Kim, "Eye correction using correlation Antananarivo. His current research is regarding image processing information", In Y. Yagi et al. (Eds.): ACCV 2007, Part I, especially using Neural Networks. LNCS 4843, pp. 698-707, 2007.  Z. Zhou, X. Geng, "Projection functions for eye detection, Andry A. Randriamitantsoa received his Engineer Diploma in Pattern Recognition", Vol. 37, pp. 1049-1056, 2004. Telecommunication on 2008 at High School Polytechnic of  J. Song, Z. Chi, J. Liu, "A robust eye detection method Antananarivo, Madagascar and his M.Sc. on 2009. Currently he is working for High School Polytechnic and he had a PhD in Automatic using combined binary edge and intensity information, and Computer Science in 2013. His research interests include Pattern Recognition", Vol. 39, pp. 1110-1125, 2006. Automatic, robust command, computer science.  T. Kawaguchi, M. Rizon, "Iris detection using intensity and edge information, Pattern Recognition", Vol. 36, pp. Paul A. Randriamitantsoa was born in Madagascar on 1953. He is 549-562, 2003. a professor at High School Polytechnic of Antananarivo and first responsible of Telecommunication- Automatic – Signal – Image  S. Asteriadis, N. Nikolaidis, A. Hajdu, I. Pitas, "An Eye Research Laboratory. Detection Algorithm Using Pixel to Edge Information", Department of Informatics, Aristotle University of Thessaloniki, Box 451, 54124, Thessaloniki, Greece, 2010.
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