Performance Evaluation of Teeth Image Recognition System Based on Difference Image Entropy

In this paper, we propose improved Difference Image Entropy (herein after, DIE)-based teeth recognition system and input image selection method. The DIE estimation module computes the DIE coefficient reflecting histogram levels have peak positions from -255 to +255, after obtains gray scaled-difference image from input teeth image and average teeth image on random-collected reference images. For performance evaluation of DIE-based teeth image recognition system including in-put image selection method, we implemented individual teeth images-based teeth image recognition system using K-NN with PCA and 2D-DCT-based EHMM pattern recognition algorithms, and then they are coupled with suggested DIE-based in-put image selection method. After that we inspect availability and validity of the application by various experiments using DIE threshold values from 6.9 to 7.3 for suitable teeth image selection. In experimental results, the suggested teeth recognition system shows approximately from 3% to 15% improved recognition performance than traditional image-based biometric algorithms / methods.