Face Recognition on Bag Locking Mechanism
AbstractWith the emergent of biometric technology, people are no longer afraid to keep their important things in the safe box or room or even facility. This is because; human beings have unique features that distinguish them with other people. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called â€˜Eigenfacesâ€™, which are actually the principal components of the initial training set of face images. In this report, thorough explanation on design process of face recognition on bags locking mechanism will be elucidated. The results and analysis of the proposed design prototype also presented and explained. The platform for executing the algorithm is on the Raspberry Pi. There are two artificial intelligent techniques applied to manipulate and processing data which is fuzzy logic and neural networks. Both systems are interdependent with each other, so that it can calculate and analyse data precisely. The receive image from the camera is analysed through the Eigenfaces algorithm. The algorithm is using Principal Component Analysis (PCA) method which comprise of artificial neural network paradigm and also statistical paradigm.
R. C. Gonzalez and R. E. Woods. â€œDigital image processingâ€, Second Edition, published by Pearson Education, 2003.
W. Zhao, R. Chellappa, J. Phillips, and A. Rosenfeld, â€œFace recognition in still and video images: A literature surveyâ€, ACM Comput Surv vol. 35, pp. 399â€“458, 2003.
J. R. Solar, and P. Navarreto, â€œEigen space-based face recognition: a comparative study of different approachesâ€, IEEE Transactions on Systems man And Cybernetics- part c: Applications, vol. 35, no.3, 2005.
M. Turk, and A. Pentland, â€œEigen faces for face recognitionâ€, Journal cognitive neuroscience, vol. 3, no.1, 1991.
W. Zhao, R. Chellappa, and A, Krishnaswamy, â€œDiscriminant analysis of principal component for face recognitionâ€, IEEE Transactions on Pattern Anal. Machine Intelligence, vol. 8, 1997.
O. Deniz, M. Castrillfion, and M. Hernfiandez, â€œFace recognition using independent component analysis and support vector machinesâ€, Pattern Recognition letters, vol. 24, pp. 2153-2157, 2003.
B. Moghaddam, â€œPrincipal manifolds and probabilistic subspaces for visual recognition", IEEE Transactions on pattern Anal. Machine Intelligence, vol. 24, no.6, pp. 780-788, 2002.
H. Othman, and T. Aboulnasr, â€œA separable low complexity 2D HMM with application to face recognitionâ€, IEEE Trans. Pattern. Anal. Machie Inell., vol. 25, no.10, pp. 1229-1238, 2003.
M. Er, S. Wu, J. Lu, and L.H.Toh, â€œface recognition with radial basis function (RBF) neural networksâ€, IEEE Transactions on Neural Networks, vol. 13, no. 3, pp. 697-710, 2003.
K. Lee, Y. Chung, and H. Byun, â€œSVM based face verification with feature set of small sizeâ€, Electronic letters, vol. 38, no.15, pp. 787- 789, 2002.
M.J.Er, W.Chen, and S.Wu, â€œHigh speed face recognition based on discrete cosine transform and RBF neural networkâ€, IEEE Trans. On Neural Network, vol. 16, no.3, pp. 679-691, 2005.
D.L. Swets and J.J. Weng , â€œUsing Discriminant Eigen features for image retrievalâ€, IEEE Trans. Pattern Anal. Machine Intel, vol. 18, pp. 831-836, 1996.
P.N. Belhumeur, J.P. Hespanha, and D. J. Kriegman, â€œEigen faces vs. Fisher faces: Recognition using class specific linear projectionâ€, IEEE Trans. Pattern Anal. Machine Intel., vol. 19, pp. 711-720, 1997.
D. Ramaeubramanian, and Y. Venkatesh, â€œEncoding and recognition of Faces based on human visual model and DCTâ€, Pattern recognition, vol. 34, pp. 2447-2458, 2001.
X. Y. Jing, and D. Zhang, â€œA face and palm print recognition approach based on discriminant DCT feature extractionâ€, IEEE trans. on Sys. Man & Cyb., vol. 34, no. 6, pp. 2405-2415, 2004.
Papoulis, and U. Pillai, â€œProbability, random variables, and Stochastic Processesâ€, McGraw-Hill, 0073660116, New York, 2002.
Haykin, S. Neural Networks: A comprehensive foundation, Prentice Hall, 0-13-273350-1, New Jersey, 1999.
How to Cite
Copyright of articles that appear in Elektrika belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.