A Passport Recognition and Face Verification Using Enhanced Fuzzy Neural Network and PCA Algorithm
In this paper, passport recognition and face verification methods which can automatically recognize passport codes and discriminate forgery passports to improve efficiency and systematic control of immigration management are proposed. Adjusting the slant is very important for recognition of characters and face verification since slanted passport images can bring various unwanted effects to the recognition of individual codes and faces. The angle adjustment can be conducted by using the slant of the straight and horizontal line that connects the center of thickness between left and right parts of the string. Extracting passport codes is done by Sobel operator, horizontal smearing, and 8-neighbornood contour tracking algorithm. The proposed RBF network is applied to the middle layer of RBF network by using the fuzzy logic connection operator and proposing the enhanced fuzzy ART algorithm that dynamically controls the vigilance parameter. After several tests using a forged passport and the passport with slanted images, the proposed method was proven to be effective in recognizing passport codes and verifying facial images.
KeywordsFacial Image Radial Basis Function Neural Network Sobel Operator Individual Code Forgery Detection
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- 4.Kim, K.B., Lim, E.K., Kim, G.H.: Analysis System of Endoscopic Image of Early Gastric Cancer. Journal of Fuzzy Logic and Intelligent Systems 15(4), 473–478 (2005)Google Scholar
- 6.Kim, K.B., Kim, Y.J.: Recognition of English Calling Cards by Using Enhanced Fuzzy Radial Basis Function Neural Networks. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E87-A(6), 1355–1362 (2004)Google Scholar