Open Access

ARTICLE

3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

Khalil Khan¹, Jehad Ali², Kashif Ahmad³, Asma Gul⁴, Ghulam Sarwar⁵, Sahib Khan⁶, Qui Thanh Hoai Ta⁷, Tae-Sun Chung⁸, Muhammad Attique^9,*

1 Department of Computer Science and Software Engineering, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur-KPK, Pakistan
2 Department of Computer Engineering and Department of AI Convergence Network, Ajou University, Suwon, 16499, South Korea
3 Hamad Bin Khalifa University, Doha, Qatar
4 Department of Statistics, Shaheed Benazir Bhutto Women University, Peshawar, Pakistan
5 Department of Software Engineering, University of Azad Jammu and Kashmir, Pakistan
6 Department of Electronics and Telecommunications, Politecnico di Torino, Torino, 10129, Italy
7 Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam
8 Department of Computer Engineering, Ajou University, Suwon, 16499, Korea
9 Department of Software, Sejong University, Seoul, 05006, South Korea

* Corresponding Author: Muhammad Attique. Email:

Computers, Materials & Continua 2021, 66(2), 1757-1770. https://doi.org/10.32604/cmc.2020.013590

Received 12 August 2020; Accepted 28 September 2020; Issue published 26 November 2020

Abstract

Face image analysis is one among several important cues in computer vision. Over the last five decades, methods for face analysis have received immense attention due to large scale applications in various face analysis tasks. Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation. In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model. We have developed an end to end face parts segmentation framework through deep convolutional neural networks (DCNNs). For training a deep face parts parsing model, we label face images for seven different classes, including eyes, brows, nose, hair, mouth, skin, and back. We extract features from gray scale images by using DCNNs. We train a classifier using the extracted features. We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class. We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase. We assess the performance of our newly proposed model on four standard head pose datasets, including Pointing’04, Annotated Facial Landmarks in the Wild (AFLW), Boston University (BU), and ICT-3DHP, obtaining superior results as compared to previous results.

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Citations