Face Alignment using Modified Supervised Descent Method
Abstract: Face alignment has
been used on preprocess stage in computer vision’s problems. One of the best
methods for face aligment is Supervised Descent Method (SDM). This method seeks
the weight of non-linear features which is used for making the product and the
feature resulting estimation on the changes of optimal distance of early
landmark point towards the actual location of the landmark points (GTS). This
article presented modifications of the SDM on the generation of some early
forms as a sample on the training stage and an early form on the test stage. In
addition, the pyramid image was used as the image for feature extraction process
used in the training phase on linear regression. 1€ filter was used to stabilize
the movement of estimated landmark points. It was found that the accuracy of
the method in BioID dataset with 1000 training images in RMSE is approximately
0.882.
Author: Mochammad Hosam,
Helmie Arif Wibawa, Aris Sugiharto
Journal Code: jptkomputergg170118
