Pose estimation using Spectral and Singular Value recomposition
Chandrasekhar Bhagavatula, Raied Aljadaany, Marios Savvides · 2016
In face recognition tasks, the changing pose of the face can cause enough information to be lost to cause the recognition to fail so being able to determine the pose of the face beforehand can allow for some better recognition performance. Many methods used for pose estimation tasks rely on finding some underlying structure of the data given to create a classifier. We propose an alternative method in which the training data itself is the underlying structure of a classifier. This is accomplished through the use of matrix decomposition equations. However, instead of decomposing a matrix, one is created by carefully selecting the terms in the decomposition equation such that the resulting matrix has the desired properties for classification. We show two recomposition methods using the Spectral Decomposition and Singular Value Decomposition equations. We show this method can perform pose estimation with a high accuracy of 85.21% and an accuracy of 98.42% when allowing a ±15° tolerance on the pose estimate on the CUbiC FacePix dataset. We also show results on both yaw and pitch estimation on the Pointing'04 dataset with our methods achieving 77.01% accuracy on yaw estimation.