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How Good Can a Face Identifier Be Without Learning
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.
2016 (English)In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349Article in journal, Editorial material (Refereed) Published
Abstract [en]

Constructing discriminative features is an essential issue in developing face recognition algorithms. There are two schools in how features are constructed: hand-crafted features and learned features from data. A clear trend in the face recognition community is to use learned features to replace hand-crafted ones for face recognition, due to the superb performance achieved by learned features through Deep Learning networks. Given the negative aspects of database-dependent solutions, we consider an alternative and demonstrate that, for good generalization performance, developing face recognition algorithms by using handcrafted features is surprisingly promising when the training dataset is small or medium sized. We show how to build such a face identifier with our Block Matching method which leverages the power of the Gabor phase in face images. Although no learning process is involved, empirical results show that the performance of this “designed” identifier is comparable (superior) to state-of-the-art identifiers and even close to Deep Learning approaches.

Place, publisher, year, edition, pages
Springer, 2016.
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-189190OAI: oai:DiVA.org:kth-189190DiVA: diva2:944067
Available from: 2016-06-28 Created: 2016-06-28 Last updated: 2016-09-14

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Zhong, YangHedman, AndersLi, Haibo
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