IJIPVS

A Modified Neural Network system based on Morphological A Modified Neural Network system based on Morphological operations for detection of images with variation in Gray level operations for detect

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Abstract

We introduce an algorithm based on the morphological shared-weight neural network. Which extract the features and then classify them. This type of network can work effectively, even if the gray level intensity and facial expression of the images are varied. The images are processed by a morphological shared weight neural network to detect and extract the features of face images. For the detection of the edges of the image we are using sobel operator. We are using back propagation algorithm for the purpose of learning and training of the neural network system. Being nonlinear and translation-invariant, the morphological operations can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The recognition efficiency of this modified network is about 98%.

Recommended Citation

 Y. Won et. al., Morphological Shared-Weight Networks with Applications to Automatic Target Recognition, Electronics and Telecommunications Research Institute, Daejon, South Korea, 1995. http://www.ieeexplore.ieee.org. [2] Y. Won and P. Gader, Morphological Shared-Weight Neural Network for Pattern Classification and Automatic Target Detection, University of Missouri Columbia, 1995. http://www.ieeexplore.ieee.org [3] Y. Won, P. Gader, and P. Coffield, “Morphological Shared- Weight Neural Networks with Applications to Automatic Target Recognition,” IEEE Transactions Neural Networks, Vol. 8, pp. 1195–1203, 1997. [4] Raman Maini and J. S. Sobel, "Performance Evaluation of Prewitt Edge Detector for Noisy Images", GVIP Journal, Vol. 6, Issue 3, December 2006. www.icgst.com [5] Davis, L. S., "Edge detection techniques", Computer Graphics Image Process. (4), 248-270, 1995. [6] M. H. Yang, D. J. Kriegman, and N. Ahuja, “Detecting Faces in Images: A Survey,” IEEE Trans, Pattern. Anal. Machine. Intell, vol. 24, no. 1, pp. 34-58, 2002. [7] A. Tefas, C. Kotropoulos, and I. Pitas, “Face Verification based Morphological Shape Decomposition,” Proc. IEEE Int'l Conference Automatic Face and Gesture Recognition, pp. 36–41, 1998 [8] Bovik, A. C., Huaung, T. S. and JR. D. C. M., "Non parametric tests for edge detection noise", Pattern Recognition, 19:209-219, 1986. [9] V. Starovoitov, D. Samal, and D. Briliuk, Three Approaches for Face Recognition, The 6-th International Conference on Pattern Recognition and Image Analysis, Velikiy Novgorod, Russia, 2002. http://www.ieeexplore.ieee.org [10] E. R. Dougherty and R. P. Loce, “Optimal mean absolute hit-or-miss filters,” Opt. Eng., vol. 32, no. 4, pp. 815–827, 1993. [11] P. Gader, J. Keller, T. Jones, and J. Miramonti,