Automatic Assessment of Eye Blinking Patterns through Statistical Shape Models

  • Federico M. Sukno
  • Sri-Kaushik Pavani
  • Constantine Butakoff
  • Alejandro F. Frangi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5815)


Several studies have related the alertness of an individual to their eye-blinking patterns. Accurate and automatic quantification of eye-blinks can be of much use in monitoring people at jobs that require high degree of alertness, such as that of a driver of a vehicle. This paper presents a non-intrusive system based on facial biometrics techniques, to accurately detect and quantify eye-blinks. Given a video sequence from a standard camera, the proposed procedure can output blink frequencies and durations, as well as the PERCLOS metric, which is the percentage of the time the eyes are at least 80% closed. The proposed algorithm was tested on 360 videos of the AV@CAR database, which amount to approximately 95,000 frames of 20 different people. Validation of the results against manual annotations yielded very high accuracy in the estimation of blink frequency with encouraging results in the estimation of PERCLOS (average error of 0.39%) and blink duration (average error within 2 frames).


Video Sequence Manual Annotation Obstructive Sleep Apnoea Syndrome False Acceptance Rate Active Shape Model 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Federico M. Sukno
    • 1
    • 2
  • Sri-Kaushik Pavani
    • 2
    • 1
  • Constantine Butakoff
    • 2
    • 1
  • Alejandro F. Frangi
    • 2
    • 1
    • 3
  1. 1.Networking Research Center on BioengineeringBiomaterials and Nanomedicine (CIBER-BBN)Spain
  2. 2.Research Group for Computational Imaging & Simulation Technologies in Biomedicine; Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
  3. 3.Catalan Institution for Research and Advanced Studies (ICREA)Spain

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