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Modified dimensionality reduced local directional pattern for facial analysis

  • S. Perumal Ramalingam
  • P. V. S. S. R. Chandra MouliEmail author
Original Research

Abstract

Local descriptors play a vital role in face analysis. This paper proposes a modified dimensionality reduced local directional pattern (MDR-LDP) for face analysis that includes both face and facial expression recognition. MDR-LDP is an updated version of dimensionality reduced local directional pattern (DR-LDP) for face recognition. DR-LDP assigns a single code for every \(3 \times 3\) region of local directional pattern (LDP) encoded image. It is a compact and efficient code to recognize faces but gives only satisfactory results for facial expression recognition. Every \(3 \times 3\) LDP encoded region is reduced to a \(2 \times 2\) region and labeled as MDR-LDP. The MDR-LDP descriptor considers the micro structure patterns in the image. The resultant MDR-LDP encoded image is further divided into regions and histograms are generated for each region. The bins of each histogram form the feature vector. The concatenation of all the feature vectors forms the MDR-LDP descriptor and is used for facial expression. The proposed MDR-LDP is robust to noise, illumination changes and pose variations. The experiments were carried out on standard databases and the objective evaluation using the standard metrics prove that MDR-LDP performs superior to the other local descriptors for face analysis.

Keywords

Local directional pattern Dimensionality reduction Facial expression Feature descriptor Face detection Image classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia
  2. 2.School of Computing Science and EngineeringVIT UniversityVelloreIndia

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