Neural Network-Based Teeth Recognition Using Singular Value Decomposition and Color Histogram

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 paper, we proposed appropriate features including machine learning model for teeth recognition. The features of our system are composed of singular values and color histogram of teeth image. 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.