Next Article in Journal
Strain Transfer for Optimal Performance of Sensing Sheet
Next Article in Special Issue
An Improved Randomized Local Binary Features for Keypoints Recognition
Previous Article in Journal
A Globally Generalized Emotion Recognition System Involving Different Physiological Signals
Previous Article in Special Issue
Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(6), 1906; https://doi.org/10.3390/s18061906

A Bayesian Scene-Prior-Based Deep Network Model for Face Verification

1,2,†,* , 2,†
,
3,†,* , 2,†,* , 1,†
and
2,*
1
Department of Electronics and Information Engineering, North China University of Technology, Beijing 100144, China
2
Department of Software, Beihang University, Beijing 100191, China
3
Department of Computing, Curtin University, Perth, WA 6102, Australia
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 12 May 2018 / Revised: 3 June 2018 / Accepted: 8 June 2018 / Published: 11 June 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
Full-Text   |   PDF [3097 KB, uploaded 14 June 2018]   |  

Abstract

Face recognition/verification has received great attention in both theory and application for the past two decades. Deep learning has been considered as a very powerful tool for improving the performance of face recognition/verification recently. With large labeled training datasets, the features obtained from deep learning networks can achieve higher accuracy in comparison with shallow networks. However, many reported face recognition/verification approaches rely heavily on the large size and complete representative of the training set, and most of them tend to suffer serious performance drop or even fail to work if fewer training samples per person are available. Hence, the small number of training samples may cause the deep features to vary greatly. We aim to solve this critical problem in this paper. Inspired by recent research in scene domain transfer, for a given face image, a new series of possible scenarios about this face can be deduced from the scene semantics extracted from other face individuals in a face dataset. We believe that the “scene” or background in an image, that is, samples with more different scenes for a given person, may determine the intrinsic features among the faces of the same individual. In order to validate this belief, we propose a Bayesian scene-prior-based deep learning model in this paper with the aim to extract important features from background scenes. By learning a scene model on the basis of a labeled face dataset via the Bayesian idea, the proposed method transforms a face image into new face images by referring to the given face with the learnt scene dictionary. Because the new derived faces may have similar scenes to the input face, the face-verification performance can be improved without having background variance, while the number of training samples is significantly reduced. Experiments conducted on the Labeled Faces in the Wild (LFW) dataset view #2 subset illustrated that this model can increase the verification accuracy to 99.2% by means of scenes’ transfer learning (99.12% in literature with an unsupervised protocol). Meanwhile, our model can achieve 94.3% accuracy for the YouTube Faces database (DB) (93.2% in literature with an unsupervised protocol). View Full-Text
Keywords: Bayesian network; deep learning network; scene transfer; deep features; face verification Bayesian network; deep learning network; scene transfer; deep features; face verification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, H.; Song, W.; Liu, W.; Song, N.; Wang, Y.; Pan, H. A Bayesian Scene-Prior-Based Deep Network Model for Face Verification. Sensors 2018, 18, 1906.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top