Chulalongkorn University Theses and Dissertations (Chula ETD)
Neural network-based teeth recognition system using hybrid features
Other Title (Parallel Title in Other Language of ETD)
ระบบรู้จำฟันบนพื้นฐานของโครงข่ายประสาทโดยใช้ลักษณะเด่นผสม
Year (A.D.)
2010
Document Type
Thesis
First Advisor
Suphakant Phimoltares
Faculty/College
Faculty of Science (คณะวิทยาศาสตร์)
Degree Name
Master of Science
Degree Level
Master's Degree
Degree Discipline
Computer Science and Information Technology
DOI
10.58837/CHULA.THE.2010.1233
Abstract
Nowadays, biometric technology is used in various security applications. The efficiency of such applications depends upon a type of biometric information. Nevertheless, some information can be faked by intent surgery or they are unexpectedly reshaped such as face, iris, palmprint and fingerprint. Unlike ordinary features, teeth cannot be easily reshaped. In this thesis, hybrid features and machine learning model for teeth recognition are proposed. Hybrid features of this system are composed of global and local features simultaneously fed into the system. In this thesis, proposed global features composed of singular values from singular value decomposition and color histogram of teeth image are analyzed and give the adequate result whilst the proposed local features are the ratio of the width from upper-front-teeth. These features were fed into the multilayer perceptron network with Levenberg-Marquart backpropagation training algorithm. With these features and model, the proposed method performs better than other existing techniques in terms of accuracy and error.
Other Abstract (Other language abstract of ETD)
Nowadays, biometric technology is used in various security applications. The efficiency of such applications depends upon a type of biometric information. Nevertheless, some information can be faked by intent surgery or they are unexpectedly reshaped such as face, iris, palmprint and fingerprint. Unlike ordinary features, teeth cannot be easily reshaped. In this thesis, hybrid features and machine learning model for teeth recognition are proposed. Hybrid features of this system are composed of global and local features simultaneously fed into the system. In this thesis, proposed global features composed of singular values from singular value decomposition and color histogram of teeth image are analyzed and give the adequate result whilst the proposed local features are the ratio of the width from upper-front-teeth. These features were fed into the multilayer perceptron network with Levenberg-Marquart backpropagation training algorithm. With these features and model, the proposed method performs better than other existing techniques in terms of accuracy and error.
Creative Commons License
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Recommended Citation
Veeraprasit, Suprachaya, "Neural network-based teeth recognition system using hybrid features" (2010). Chulalongkorn University Theses and Dissertations (Chula ETD). 60239.
https://digital.car.chula.ac.th/chulaetd/60239