Facial Analysis Using Jacobians and Gradient Boosting

  • A. Vinay
  • Abhijay GuptaEmail author
  • Vinayaka R. Kamath
  • Aprameya Bharadwaj
  • Arvind Srinivas
  • K. N. B. Murthy
  • S. Natarajan
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 308)


Security and identity have become one of the primary concerns of the people in this digital world. Person authentication and identification is transforming the way these services are provided. Earlier it was mainly achieved through passwords and patterns but with significant advancements in face recognition technologies, it has been exploited in providing authentication in smart phones and computers. Face Recognition (FR) extends its use in applications like face tagging in social media, surveillance system at theaters, airports and so on. The proposed mathematical model employs linear algebra and mathematical simulations for the task of person identification. Kernel singular value decomposition is used to denoise the facial image which is then passed to a feature detector and descriptor based on nonlinear diffusion filtering. The obtained descriptors are quantized into a vector using an encoding model called VLAD which uses k-means++ for clustering. Further, classification is done using Gradient boosting decision trees. The pipeline proposed aims at reducing the average computational power and also enhancing the efficiency of the system. The proposed system has been tested on the three benchmark datasets namely Face 95, Face 96 and Grimace.


Linear algebra Kernel-SVD Feature quantization Gradient boosted decision tree 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • A. Vinay
    • 1
  • Abhijay Gupta
    • 1
    Email author
  • Vinayaka R. Kamath
    • 1
  • Aprameya Bharadwaj
    • 1
  • Arvind Srinivas
    • 1
  • K. N. B. Murthy
    • 1
  • S. Natarajan
    • 1
  1. 1.Centre for Pattern Recognition and Machine IntelligencePES UniversityBengaluruIndia

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