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Online Face Recognition System Based on Local Binary Patterns and Facial Landmark Tracking

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9386))

Abstract

This paper presents a system for real-time face recognition. The system learns and recognizes faces from a video on the fly and it doesn’t need already trained database. The system consists of the following sub-methods: face detection and tracking, face alignment, key frame selection, face description and face matching. The system detects face tracks from a video, which are used in learning and recognition. Facial landmark tracking is utilized to detect changes in facial pose and expression in order to select key frames from a face track. Faces in key frames are represented using local binary patterns (LBP) histograms. These histograms are stored into the database. Nearest neighbor classifier is used in face matching. The system achieved recognition rate of 98.6% in offline test and 95.9% in online test.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Oulu, P.O. Box 4500, 90014, Oulu, Finland

    Marko Linna, Juho Kannala & Esa Rahtu

Authors
  1. Marko Linna
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  2. Juho Kannala
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  3. Esa Rahtu
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Corresponding author

Correspondence to Marko Linna .

Editor information

Editors and Affiliations

  1. Dipartimento di Matematica e Informatica, UniversitĂ  di Catania, Catania, Catania, Italy

    Sebastiano Battiato

  2. Arcueil CX, France

    Jacques Blanc-Talon

  3. Catania, Italy

    Giovanni Gallo

  4. Gent, Belgium

    Wilfried Philips

  5. CSIRO, Sydney, New South Wales, Australia

    Dan Popescu

  6. Vision Lab., University of Antwerp, Antwerpen, Belgium

    Paul Scheunders

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© 2015 Springer International Publishing Switzerland

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Linna, M., Kannala, J., Rahtu, E. (2015). Online Face Recognition System Based on Local Binary Patterns and Facial Landmark Tracking. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_35

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  • DOI: https://doi.org/10.1007/978-3-319-25903-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25902-4

  • Online ISBN: 978-3-319-25903-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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