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Unconstrained and NIR Face Detection with a Robust and Unified Architecture

  • Priyabrata Dash
  • Dakshina Ranjan Kisku
  • Jamuna Kanta Sing
  • Phalguni Gupta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)

Abstract

This paper proposes a face detection method making use of Fast Successive Mean Quantization Transform (FSMQT) features for image representation to deal with illumination and sensor insensitive issues of the individual as well as the crowd face images. A split up Sparse Network of Winnows (SNoW) with Winnow updating rule is then exploited to speed up the original SNoW classifier. Features and classifiers are combined together with skin detection algorithm for fake face detection in crowd image and head orientation correction for near infrared faces. The experiment is performed with four databases, viz. BIOID, LFW, FDDB and IIT Delhi near infrared showing superior performance.

Keywords

Face detection Fast SMQT Split up SNOW classifier Pose Occlusion Blur Labeled faces Crowd faces 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Priyabrata Dash
    • 1
  • Dakshina Ranjan Kisku
    • 1
  • Jamuna Kanta Sing
    • 2
  • Phalguni Gupta
    • 3
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology DurgapurDurgapurIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  3. 3.National Institute of Technical Teachers Training and ResearchSalt Lake, KolkataIndia

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