Face Recognition Using Combined DRLBP & SIFT Features Using Arduino Uno 328

  • Miss. Seema Atole
  • Ms. J. A. Kendule
Keywords: Facial Recognition, SIFT Features, DRLBP, Fuzzy Classifier, ARDUINO UNO 328


In this paper, face recognition is proposed using combined DRLBP and SIFT features with the help of ARDUINO UNO 328 for high efficient signal transfer system applications. The aim of this research is to develop a nonreal-life application of a security lock system employing a face recognition methodology. DRLBP is chosen for the face recognition algorithmic program. Arduino microcontroller is employed to represent the response to face identification. USB serial communication is employed to interface between the MATLAB and Arduino UNO Microcontroller. 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, the result is sent to the Arduino UNO microcontroller.


[1] Zheng yu; Fen Liu; Rongtao Liao; Yixi wang; hao Feng; Xiaojun Zhu; “Improvement of face recognition algorithm based on Neural network” 2018 10th international conference on measuring technology and mechatronics automation(ICMTMA).
[2] Srivignessh pss,Bhaskar .M “RFID & Pose Invariant Face Verification Based Automated Classroom Attendance System" international conference IEEE. 2016.
[3] Zhi Li; “A discriminative learning convolutional neural network for facial expression recognition” 2017 3rd IEEE international conference on computer and communications (ICCC).
[4] Abir Fathallah; Lotfi Abdi; Ali Douik; “Facial expression recognition via deep learning” 2017 IEEE/ACS 14th International conference on computer systems and applications (AICCSA).
[5] Hicham HATIMI & Mohamed FAKIR “Face recognition using a fuzzy approach and a multi-agent system from video Sequences”. 978-1-5090-0811-7/16 $31.00 © 2016 IEEEDOI 10.1109/ CGiV.2016.91
[6] Yong-Ping Chen, Qi-Hui Chen, Kuan-Yu Chou* and Ren-Hau Wu“Low-Cost Face Recognition System Based on Extended Local Binary Pattern” 2016 IEEE
[7] Chenghua Li , Qi Kang & Guojing Ge “DeepBE: Learning Deep Binary Encoding for Multi-Label Classification”. 2016 IEEE
Conference on Computer Vision and Pattern Recognition Workshops
[8] Pomthep Sarakon, Theekapun Charoenpong “Face Shape Classification from 3D Human Data by using SVM”. 978-1-4799-6801-5/ 14/$3l.00 ©2014 IEEE
[9] Hai-Wen Chen&Mike McGurr “Improved Color and Intensity Patch Segmentation for Human Full-Body and Body-Parts Detection and Tracking”2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
[10] Meng Yang, Member “Regularized Robust Coding for Face Recognition”. IEEE transactions on image processing, vol. 22, no. 5, may 2013
[11] Seyed Mehdi Lajevardi, Member, “Facial Expression Recognition in Perceptual Color Space” IEEE transaction on image processing, vol.21,no8, august 2012.
[12] Hongliang Jin, Qingshan Liu, Hanqing Lu, Xiaofeng Tong “Face Detection Using Improved LBP Under Bayesian Framework” Third International Conference on Image and Graphics (ICIG’04) 2004 IEEE
How to Cite
Atole, M. S., & Kendule, M. J. A. (2018, November 2). Face Recognition Using Combined DRLBP & SIFT Features Using Arduino Uno 328. ASIAN JOURNAL FOR CONVERGENCE IN TECHNOLOGY (AJCT ) -UGC LISTED, 4(II). Retrieved from http://asianssr.org/index.php/ajct/article/view/560