Digital Library

cab1

 
Title:      REAL-TIME HEAD POSE ESTIMATION WITH SVM MODEL FOR FRONTAL FACE CLASSIFICATION
Author(s):      Loubrys L. Rojas Reinoso, Fernando L. Gutiérrez López, José C. Gutiérrez, Graça Bressan and Wilson Vicente Ruggiero
ISBN:      978-989-8533-99-9
Editors:      Piet Kommers, Adriana Backx Noronha Viana, Theodora Issa, Pedro Isaías and Tomayess Issa
Year:      2020
Edition:      Single
Keywords:      Degree of Freedom of Human Head (DOF), Head Pose Estimation (HPE), Support Vector Machine (SVM), Frontal Face Classification, Machine Learning, Computer Vi
Type:      Short
First Page:      63
Last Page:      67
Language:      English
Cover:      cover          
Full Contents:      click to dowload Download
Paper Abstract:      Head pose estimation (HPE) has been widely studied in the last years due to its many applications in face analysis systems. The use of such systems ranges from the analysis of focus of attention, social interactions or the use in mobile applications in the realization of the currently popular facial animations and / or in face recognition process, where the frontal faces are especially important. Many approaches were proposed focusing mainly in Random Forests and Convolutional Neural Networks (CNN). In this paper, a framework for estimation of the head pose was proposed computing the degrees of freedom (DOF) of the human head using 2D images data only. The framework implements some computer vision algorithms available in publicly machine learning libraries such as OpenCV and Dlib, which allows easy application and re-implementation. In addition, a Support Vector Machine (SVM) model with Radial Basis Function (RBF) kernel was developed for frontal face classification. Experiments conducted on 2D image datasets in constrained environment show that the approach is capable of real-time performance. Were designed three protocols of experiments with two databases for testing the SVM model. Values of 100% and 98% for precision and recall, respectively, were achieved classifying frontal faces. Significant results were obtained measuring yaw rotation with 4.24 of mean absolute error for frontal face.
   

Social Media Links

Search

Login