Image-Based Driver Drowsiness Detection

  • F. DornaikaEmail author
  • F. Khattar
  • J. Reta
  • I. Arganda-Carreras
  • M. Hernandez
  • Y. Ruichek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11264)


How to extract effective features of fatigue in images and videos is important for many applications. This paper introduces a face image descriptor that can be used for discriminating driver fatigue in static frames. In this method, first, each facial image in the sequence is represented by a pyramid whose levels are divided into non-overlapping blocks of the same size, and hybrid image descriptor are employed to extract features in all blocks. Then the obtained descriptor is filtered out using feature selection. Finally, non-linear Support Vector Machines is applied to predict the drowsiness state of the subject in the image. The proposed method was tested on the public dataset NTH Drowsy Driver Detection (NTHUDDD). This dataset includes a wide range of human subjects of different genders, poses, and illuminations in real-life fatigue conditions. Experimental results show the effectiveness of the proposed method. These results show that the proposed hand-crafted feature compare favorably with several approaches based on the use of deep Convolutional Neural Nets.


Drowsiness detection Hand-crafted features Deep features Supervised classification 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • F. Dornaika
    • 1
    • 2
    Email author
  • F. Khattar
    • 1
  • J. Reta
    • 1
  • I. Arganda-Carreras
    • 1
    • 2
  • M. Hernandez
    • 1
  • Y. Ruichek
    • 3
  1. 1.University of the Basque Country UPV/EHUSan SebastianSpain
  2. 2.IKERBASQUE, Basque Foundation for ScienceBilbaoSpain
  3. 3.LE2i, CNRS, University of Bourgogne Franche-ComteBelfortFrance

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