Influence of Parameters on Landmark Automatic Identification from Three Dimensional (3D) Data

Abstract

Local feature extraction is one of the fundamental aspects of the Three Dimensional (3D) data process and thus is quite promising. In our previous study, an algorithm combining Spin Image (SI) with Hidden Markov Model (HMM), was developed and applied on a 200 people database to automatically identify facial landmarks from 3D face data. The purpose of this work is to evaluate the reliability and accuracy of facial landmark identification with different parameter combinations, i.e. Bin Size (BS) and Support Angle (SA). Bin Size can improve or reduce Identification Accuracy Rate (IAR) by its value. The mean value of IAR increases with the Bin Size until the size reaches 10, when IAR acquires its maximum value 100% and remains constant before the Bin Size reaches 65. After that, IAR dropped with the increase of Bin Size, the velocity of the drop keeps increasing. Support Angle influences IAR positively. Support Angle starts to function at the value of 10 degrees, then, IAR increases with it until it reaches the degree of 90, when IAR acquired and maintained a constant maximum value of 100%. There are still several aspects need to be further studied such as efficiency and robustness. Moreover, using our method to identify landmarks on other human body segments is worth more investigation.

Keywords: Parameters influence; Landmark Automatic Identification; Three Dimensional Data; Spin Image (SI); Hidden Markov Model (HMM);

DOI: 10.54941/ahfe100075