Paper Info
Title
Head Pose Estimation By Regression Algorithm
Abstract
Head pose estimation is a very in-depth topic in the context of biometric recognition, especially in video surveillance, because the rotation of the head can affect the recognition of some features of the face. Being able to recognize in advance the pose of the head in pitch, yaw and roll enable frontalization or the extraction of a frame in which a face is frontal in order to allow a more accurate recognition. In this work the Web-Shaped Model algorithm is used for a coding of the pose of the face and then we apply regression algorithms to predict the pose of the face. The proposed approach stimulates the sensitivity of the regression methods to identify the head pose estimation. The goals is to predict the value of the dependent variable for the three angular values, for which some information relating to the explanatory variables is available, in order to estimate the effect on the dependent variable. The presented method is tested on some of the most well-known datasets for the head pose estimation as Biwi, AFLW2000 and Pointing'04 and compared with the various state of the art methods that use these datasets. (C) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2020
10.1016/j.patrec.2020.10.003
PATTERN RECOGNITION LETTERS
Keywords
DocType
Volume
Head pose estimation, Regression algorithm, Image analysis
Journal
140
ISSN
Citations 
PageRank 
0167-8655
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Andrea Francesco Abate145834.51
Paola Barra212.72
Pero Chiara311.72
Maurizio Tucci424054.91