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Leveraging Gabor Phase for Face Identification in Controlled Scenarios
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-8673-0797
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.ORCID iD: 0000-0003-3779-5647
2016 (English)In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Science and Technology Publications,Lda , 2016, 49-58 p.Conference paper, Published paper (Refereed)
Abstract [en]

Gabor features have been widely employed in solving face recognition problems in controlled scenarios. To construct discriminative face features from the complex Gabor space, the amplitude information is commonly preferred, while the other one — the phase — is not well utilized due to its spatial shift sensitivity. In this paper, we address the problem of face recognition in controlled scenarios. Our focus is on the selection of a suitable signal representation and the development of a better strategy for face feature construction. We demonstrate that through our Block Matching scheme Gabor phase information is powerful enough to improve the performance of face identification. Compared to state of the art Gabor filtering based approaches, the proposed algorithm features much lower algorithmic complexity. This is mainly due to our Block Matching enables the employment of high definition Gabor phase. Thus, a single-scale Gabor frequency band is sufficient for discrimination. Furthermore, learning process is not involved in the facial feature construction, which avoids the risk of building a database-dependent algorithm. Benchmark evaluations show that the proposed learning-free algorith outperforms state-of-the-art Gabor approaches and is even comparable to Deep Learning solutions.

Place, publisher, year, edition, pages
Science and Technology Publications,Lda , 2016. 49-58 p.
Keyword [en]
Face Recognition, Controlled Scenario, HD Gabor Phase, Block Matching, Learning-free, Deep Learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-189188DOI: 10.5220/0005723700490058OAI: oai:DiVA.org:kth-189188DiVA: diva2:944056
Conference
International Conference on Computer Vision Theory and Applications, Rome, Italy, 27-29 February 2016
Note

QC 20160715

Available from: 2016-06-28 Created: 2016-06-28 Last updated: 2016-11-01Bibliographically approved
In thesis
1. Human Face Identification and Face Attribute Prediction: From Gabor Filtering to Deep Learning
Open this publication in new window or tab >>Human Face Identification and Face Attribute Prediction: From Gabor Filtering to Deep Learning
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

After decades of research, it is exciting to see that face recognition technology has entered a most flourishing era. Driven by the latest development in data science and especially technical evolutions in computer vision and pattern recognition, face recognition has achieved significant progress over the last three years. In the near future, people can expect many useful and interesting face recognition applications to be deployed in many situations: they can be used for identifying suspects, organizing your photos with family and friends, and making computers better understand human beings. Many mysterious face recognition tricks depicted in movies may become reality in several years' time.

This thesis focuses on the development of face recognition algorithms that identify people from a single still image. Two questions are specifically studied. First, it introduces how we identify faces captured in controlled scenarios with cooperative users. In this scenario, a face recognition system captures a face and finds the most similar face from the ones stored in the face recognition system. Second, it describes our solutions for predicting face attributes from faces captured under arbitrary imaging conditions. These two problems were tackled by different schools of technologies: the solution to the first question employed a learning-free approach, whereas the latter question was solved by using the most recent Deep Learning technology. Thus, this thesis also reflects the technological evolution of face recognition over recent years.

To identify faces in controlled scenarios, we propose a novel Block Matching approach, which can effectively match faces without feature engineering or any machine learning components. By representing faces with very concise Gabor phase codes and matching them through our Block Matching approach, the identification accuracy is entirely comparable to and even better than the state-of-the-art. For predicting the attributes from faces captured in the wild, we propose leveraging the off-the-shelf mid-level representations from pre-trained convolutional neural networks. Comparative experiments show that our solution outperforms the previous state-of-the-art solution with a large margin in terms of both accuracy and efficiency. 

The approaches described in this thesis may look different from the ``mainstream''. But, together with the empirical findings, I hope they could provide some insights and update widely adopted concepts for solving related face recognition and computer vision problems.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 27 p.
Series
TRITA-CSC-A, ISSN 1653-5723
National Category
Engineering and Technology
Research subject
Media Technology
Identifiers
urn:nbn:se:kth:diva-195092 (URN)978-91-7729-156-5 (ISBN)
Public defence
2016-11-11, K1, 13:00 (English)
Opponent
Supervisors
Note

QC 20161103

Available from: 2016-11-03 Created: 2016-11-01 Last updated: 2016-11-16Bibliographically approved

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Citation style
  • apa
  • harvard1
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