Facial Recognition Performance Based on the Lighting Set-Up Models Applied to Home Security Door Access using Principal Component Analysis and Raspberry Pi Controller


Authors
  • Anna Liza A. Ramos
  • Bless L. Reyes
  • Jomar J. Nuevo
  • Paulo A. Avila
  • Eugene A. Bas
  • Patrick June M. Gonzales
  • St. Michael's College of Laguna
Published in


Abstract
  • Security protects individuals, data, and properties together with its corresponding measures in line to the emerging application of technology like the face recognition. This study aims to test the effects of lighting models on the recognition performance along with different angles and distance applied for door access by providing signals to Raspberry PI Controller. The study built 240 training datasets and applied the best algorithms – Haar-Cascade for face detection, Principal Component Analysis for extraction, Support Vector Machine for classification and Euclidean Distance for recognition. The study tested the model with five subjects which then marked a score of 100% for fluorescent light, 74.65 % for candle light, and 88 % for flashlight with an average percentage accuracy of 89.5. The results implied an adverse recognition result influenced by the lighting conditions, the face angle positions and distance issues.


Keywords
  • Security, facial recognition, facial detection, lighting model, IOT


Download Papers


Cite As
  • APA 7th Edition:
    Ramos, A., Reyes, B., Nuevo, J., Avila, P., Bas, E., & Gonzales, P. (2019). Facial Recognition Performance Based on the Lighting Set-Up Models Applied to Home Security Door Access using Principal Component Analysis and Raspberry Pi Controller. Innovatus, 2(1), 40-46.
  • Harvard:
    Ramos, A., Reyes, B., Nuevo, J., Avila, P., Bas, E. and Gonzales, P., 2019. Facial Recognition Performance Based on the Lighting Set-Up Models Applied to Home Security Door Access using Principal Component Analysis and Raspberry Pi Controller. Innovatus, 2(1), pp.40-46.
  • IEEE:
    [1] A. Ramos, B. Reyes, J. Nuevo, P. Avila, E. Bas and P. Gonzales, "Facial Recognition Performance Based on the Lighting Set-Up Models Applied to Home Security Door Access using Principal Component Analysis and Raspberry Pi Controller", Innovatus, vol. 2, no. 1, pp. 40-46, 2019.


References
  • Manjunatha R. and Dr. Nagaraja R. (2017) Home Security System and Door Access Control Based on Face Recognition, International Research Journal of Engineering and Technology, Volume: 04 Issue: 03 | Mar -2017.
  • Sharma N. (2016). Home Security System Based on Sensors and IOT. International Journal of Innovative Research in Science Engineering and Technology, Vol. 5, Issue 6, June 2016
  • Massaro G. A., Barbuzzi D., Legrottagli M. (2016) "Face Recognition System on Mobile Device based on Web Service approach ", (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 7 (4), 2016
  • Amanullah M. (2013) “Microcontroller Based Reprogrammable Digital Door Lock Security System by Using Keypad & Gsm/Cdma Technology” Informing Science Special Issues on Multimedia Informing Technologies. Volume 3 No. 1, 2000
  • Shetty N.R. and Prathviraj N. (2017) Advanced PCA for Enhanced Illumination in Face Recognition to Control Smart Door Lock System, International Journal of Internet of Things, Vol. 6 No. 2, 2017, pp. 34-39.
  • Shariff S.A, Amaranatha C., Jadhav R.A, Dr. Babu K.S, Hussain M. (2016) Face and Bio-Metric Based Attendance and Security System using RFID and Arduino, International Journal of Electrical Electronics & Computer Science Engineering, Special Issue - NEWS 2016
  • Poon B., Amin M.A. and Yan H. (2009) “PCA based Face Recognition and Testing Criteria” Machine Learning and Cybernetics, 2009 International Conference on, Volume: 5 2009.
  • Khan S.R, Mansur A.A., Kabir A., Jaman S., Chowdhury N. (2012) “Design and Implementation of Low-Cost Home Security System Using GSM Network” International Journal of Scientific & Engineering Research Volume 3, Issue 3, March -2012
  • Pavel S. (2015) Smart Lock System, Vaasan ammattikorkeakoulu university of applied sciences, 2015
  • Mrutyunjanya S. (2014) Design of Face Recognition based Embedded Home Security System. Transactions on Internet and Information Systems (TIIS) 2016. Apr, 10
  • Ao M., Yi D., Lei Z., and. Li S. Z (2009) Face Recognition at a Distance: System Issues, Center for Biometrics and Security Research and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing 100190, China
  • Dabhade S. A. and Prof. Bewoor M. S. (2012) Real Time Face Detection and Recognition using Haar - Based Cascade Classifier and Principal Component Analysis, International Journal of Computer Science and Management Research - Volume 1 Issue 1 Aug 2012
  • Narayana K. V., Manoj V. V. R., Swathi K.(2015) Enhanced Face Recognition based on PCA and SVM. International Journal of Computer Applications. Volume 117 – No. 2, May 2015
  • Sharma S. And Sachdeva K. (2015) Face Recognition using PCA and SVM with Surf Technique. International Journal of Computer Applications. Volume 129 – No.4, November 2015
  • Hemlata1 A., Motwani M. (2013) Face Detection by Finding the Facial Features and the Angle of Inclination of Tilted Face. IJCSI International Journal of Computer Science Issues. Vol. 10, Issue 2, No 1, March 2013, ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784
  • Sunita Devi1 N., Hemachandran K. (2014) Face Recognition Using Principal Component Analysis, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (5), 2014, 6491-6496
  • Melo E. F., De Oliveira H. M. (2011) A Fingerprint-based Access Control using Principal Component Analysis and Edge Detection
  • Aithal M. B., Gaikwad P. R., and Sahare S. L. (2015) Speech Enhancement Using PCA for Speech and Emotion Recognition, G.J. E.D.T., Vol.4(3):6-12
  • Madge S. (2015) Predicting Stock Price Direction using Support Vector Machines, Independent Work Report Spring 2015
  • Loussaief S., Abdelkrim A. (2018) Machine Learning framework for image classification, Advances in Science, Technology and Engineering Systems Journal Vol. 3, No. 1, 01-10
  • Yahyaoui A., Yumuşak N. (2018) Decision support system based on the support vector machines and the adaptive support vector machines algorithm for solving chest disease diagnosis problems, Biomedical Research 2018; 29 (7): 1474-1480
  • Padilla R., Costa Filho C. F. F and Costa M. G. F. (2012) Evaluation of Haar Cascade Classifiers for Face Detection, World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol: 6, No:4, 2012
  • Chen Q., Georganas N. D., Petriu E. M. (2007) Real-time Vision-based Hand Gesture Recognition Using Haar-like Features, Instrumentation and Measurement Technology Conference – IMTC 2007
  • Moghimi M. M., Nayeri M., Pourahmadi M. and Moghimi M. K. (2017) Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos, 3Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran


Cited By
  • No citations found yet



Back to Archive
About Us
Contact Us

[innovatus@uap.asia] | [Pearl Dr, Ortigas Center, Pasig, 1605, Metro Manila]