Face Detection and Recognition Using Combined DRLBP and Sift Features with Fuzzy Classifier

  • Seema AtoleEmail author
  • J. A. Kendule
Conference paper


In this research, we have proposed face recognition using combined DRLBP and SIFT features using fuzzy classifier. In recent years, security systems became one among the most exacting systems to secure our assets and defend our privacy. A lot of reliable security system should be developed to avoid losses because of identity theft or fraud. Thus, lots of research has been done to enhance an established security system, particularly systems that area unit supported human identification.” Face recognition system is widely employed in human identification method due to its capability to live and later on establish human identification particularly for security functions. The aim of this project is to develop a non-real-life application of a security lock system employing a face recognition methodology. Dominant rotated local binary pattern (DRLBP) is chosen for the face recognition algorithmic program thanks to its quick response of recognition method and less sensitiveness to noise and interference. First, the image of the individual is captured then the captured image is then transferred to the information developed in MATLAB. During this stage, the captured image compares to the training image within the database to see the individual standing. If the system acknowledges the individual as an authentication person or un-authentication person.


Facial recognition Facial identification SIFT features DRLBP Fuzzy classifier 


  1. 1.
    Kar N, Debbarma MK, Saha A, Pal DR (2010) Study of implementing automated attendance system using face recognition technique. In: Information Technology Interfaces (ITI), 2010 32nd global conference on IEEEGoogle Scholar
  2. 2.
    Sarakon P, Charoenpong T, Charoensiriwath S (2014) Face shape classification from 3D human data by using SVM. In: The 2014 Biomedical Engineering International conference (BMEiCON-2014)Google Scholar
  3. 3.
    Wang X, Cai Y, Abdulghafour M (2015) A comprehensive assessment system to optimize the overlap in DCT-HMM for face recognition. In: 11th international conference on Innovations in Information Technology (IIT) 2015 IEEEGoogle Scholar
  4. 4.
    Srivignessh PSS, Bhaskar M (2016) RFID & pose invariant face verification based automated classroom attendance system. International conference IEEE.
  5. 5.
    Srivignessh PSS, Bhaskar M (2016) RFID & Pose invariant face verification based automated classroom attendance system. International Conference IEEEGoogle Scholar
  6. 6.
    Kuriakose RB Vermaak HJ (2015) Developing a Java based RFID application to automate student attendance monitoring 2015. In: Pattern Recognition Association of South Africa and Robotics and Mechatronics International conference (PRASA-RobMech) Port Elizabeth, South Africa, November 26–27, IEEEGoogle Scholar
  7. 7.
    Yu Z, Liu F, Liao R, Wang Y, Feng H, Zhu X (2018) Improvement of face recognition algorithm based on neural network 2018. In: 10th international conference on measuring technology and mechatronics automation (ICMTMA)Google Scholar
  8. 8.
    Li Z (2017) A discriminative learning convolutional neural network for facial expression recognition. In: 3rd IEEE international conference on computer and communications (ICCC)Google Scholar
  9. 9.
    Fathallah A, Abdi L, Douik A (2017) Facial expression recognition via deep learning. In: IEEE/ACS 14th international conference on computer systems and applications (AICCSA)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.SVERI’s College of EngineeringPandharpurIndia

Personalised recommendations