Scaled Conjugate Gradient Algorithm and SVM for Iris Liveness Detection

  • Manjusha N. ChavanEmail author
  • Prashant P. Patavardhan
  • Ashwini S. Shinde
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


With the extensive use of biometric systems at most of the places for authentication, there are security and privacy issues concerning with it. Most of the public places we usually visit are under reconnaissance and may keep track of face, fingerprints, and iris etc. Thus, biometric information is not secret anymore and sensitive to spoofing attacks. Therefore, not only biometric traits but also liveness detection must be deployed in an authentication mechanism which is a challenging task. Iris is the most accurate trait and increasingly in demand in applications like national security, duplicate-free voter registration list, and Aadhar program its detection must be made robust. Solution to the iris liveness detection by extracting distinctive textural features from genuine (live) and fake (print) patterns using statistical approaches GLCM and GLRLM are implemented. Popular supervised SVM algorithm and PatternNet neural network with second-order scaled conjugate gradient training algorithm are assessed. Both of these algorithms are found to be faster with PatternNet outperforms over SVM.


Textural features Gray-level co-occurrence matrix (GLCM) Gray-level run length matrix (GLRLM) Scaled conjugate gradient training algorithm(SCG) SVM 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Manjusha N. Chavan
    • 1
    Email author
  • Prashant P. Patavardhan
    • 2
  • Ashwini S. Shinde
    • 1
  1. 1.E & TC DepartmentADCETAshtaIndia
  2. 2.E & C DepartmentGIT BelgaumBelgaumIndia

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