IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN NANO SCALE ENVIRONMENT
AbstractFacial recognition systems are computer-based security systems that are able to automatically detect and identify human faces. Facial recognition has gained increasing interest in the recent decade. Over the years there have been several techniques being developed to achieve high success rate of accuracy in the identification and verification of individuals for authentication in security systems. This project experiments the concept of neural network for facial recognition that can differentiate and recognize face of image. This face recognition system begins with image pre-processing and then the output image is trained using Fuzzy c-means clustering (FCM) algorithm. FCM network learns by training the inputs, calculating the error between the real output and target output, and propagates back the error to the network to modify the weights until the desired output is obtained. After training the network, the recognition system is tested to ensure that the system can recognize the pattern of each face image. The purpose of this project is to recognize face of image for the recognition analysis using Neural Network and capture the brainwaves of the emotion recognition. This project is mainly concern with facial recognition systems using purely image processing technique.
J. C. Bezdek, Fuzzy mathematics in pattern classification,â€ Ph.D. dissertation,Appl. Math. Center, Cornell Univ., Ithaca, NY, 1973.
B. Biswas, A. K. Mukherjee, and A. Konar, Matching of digital images using fuzzy logic,â€ AMSE Publication, vol. 35, no. 2, pp. 7â€“11, 1995.
M. T. Black and Y. Yacoob, Recognizing facial expressions in image sequences using local parameterized models of image motion,â€ Int. J.Comput. Vis., vol. 25, no. 1, pp. 23â€“48, Oct. 1997.
sexC. Busso and S. Narayanan, Interaction between speech and facial gesturesin emotional utterances: A single subject study,â€ IEEE Trans. Audio, Speech Language Process., vol. 15, no. 8, pp. 2331â€“2347, Nov. 2007.
I. Cohen, Facial expression recognition from video sequences,â€ M.S.thesis, Univ. Illinois Urbana-Champaign, Dept. Elect. Eng., Urbana, IL,2000.
I. Cohen, N. Sebe, A. Garg, L. S. Chen, and T. S. Huang, Facial expression recognition from video sequences: Temporal and static modeling,â€Comput. Vis. Image Underst., vol. 91, no. 1/2, pp. 160â€“187, Jul. 2003.
C. Conati, Probabilistic assessment of userâ€™s emotions in educational games,â€ J. Appl. Artif. Intell., Special Issue Merging Cognition Affect, vol. 16, no. 7/8, pp. 555â€“575, Aug. 2002.
G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman, and T. J. Sejnowski,Classifying facial actions,â€ IEEE Trans. Pattern Anal. Mach. Intell.,vol. 21, no. 10, pp. 974â€“989, Oct. 1999.
P. Ekman and W. V. Friesen, Unmasking the Face: A Guide to Recognizing Emotions From Facial Clues. Englewood Cliffs, NJ: Prentice-Hall,1975.
I. A. Essa and A. P. Pentland, Coding, analysis, interpretation and recognition of facial expressions,â€ IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 757â€“763, Jul. 1997.
W.A.Fellenz, J. G. Taylor, R. Cowie, E. Douglas-Cowie, F. Piat, S. Kollias, C. Orovas, and B. Apolloni, On emotion recognition of faces and of speech using neural networks, fuzzy logic and the ASSESS systems,â€in Proc. IEEE -INNS-ENNS Int. Joint Conf. Neural Netw., 2000,pp. 93â€“98.
J. M. Fernandez-Dols, H. Wallbotl, and F. Sanchez, Emotion category accessibility and the decoding of emotion from facial expression and context,â€ J. Nonverbal Behav., vol. 15, no. 2, pp. 107â€“123,Jun. 1991