CFP last date
20 January 2025
Reseach Article

Real Face Detection and Recognition: The Live Experiment

by Shailesh Wadhankar, Priya Singh, Soumyakant Sahoo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 27
Year of Publication: 2018
Authors: Shailesh Wadhankar, Priya Singh, Soumyakant Sahoo
10.5120/ijca2018916645

Shailesh Wadhankar, Priya Singh, Soumyakant Sahoo . Real Face Detection and Recognition: The Live Experiment. International Journal of Computer Applications. 180, 27 ( Mar 2018), 20-27. DOI=10.5120/ijca2018916645

@article{ 10.5120/ijca2018916645,
author = { Shailesh Wadhankar, Priya Singh, Soumyakant Sahoo },
title = { Real Face Detection and Recognition: The Live Experiment },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 180 },
number = { 27 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 20-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number27/29145-2018916645/ },
doi = { 10.5120/ijca2018916645 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:01:57.226881+05:30
%A Shailesh Wadhankar
%A Priya Singh
%A Soumyakant Sahoo
%T Real Face Detection and Recognition: The Live Experiment
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 27
%P 20-27
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face Recognition is one of the most widely researched and challenging fields in computer vision and machine learning. Dynamism of human face poses several challenges in developing a machine learning algorithm or a deep learning model for face recognition across different environments. This mix of human face dynamism and altering environmental factors leads to inaccurate face recognition. The goal of this paper is to propose a face recognition model, below are the multiple actions taken to finalize the model. Framework Selection: Open Face framework and linear SVM classifier to recognize a person’s face after comparing with other models or frameworks available with the help of live experimentation on human faces Live Face Recognition activity: Two rounds of Crowd testing has been conducted at Persistent Systems Pune & Nagpur offices. Crowd Test 1 (CT1): 223(86 and 137 in two batches) candidates, 40 images each. Systems were trained daily with new images collected in the process. Crowd Test 2 (CT2): 81 candidates, 80 images each. System has been trained only for last day of testing. Total daily score was higher than CT1, as the system was trained with double the number of images.

References
  1. Mandeep Kaur and Jasjit Kaur (2017) “Review of Face Recognition Techniques.” In: International Journal of Computer Applications (0975 – 8887) Volume 164 – No 6
  2. Zhang W. and Guo Y. (2000) “Feature-Based Face Recognition: Neural Network Using Recognition-by-Recall” In:Mizoguchi R., Slaney J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science, vol 1886. Springer, Berlin, Heidelberg
  3. Xiaoyang Tan, Songcan Chen, Zhi-Hua Zhou, Fuyan Zhang (2006) “Face recognition from a single image per person: A survey” In: Journal Pattern Recognition Archive Volume 39 Issue 9, September, 2006 Pages 1725-1745
  4. Ranjana Sikarwar and Pradeep Yadav (2017) “An Approach to Face Detection and Feature Extraction using Canny Method” In: International Journal of Computer Applications (0975 – 8887) Volume 163 – No 4, April 2017
  5. Arai, K. andMardiyanto R (2011) “Comparative Study on Blink Detection and Gaze Estimation Methods for HCI, in Particular, Gabor Filter Utilized Blink Detection Method” In: Proceedings of the 2011 Eighth International Conference on Information Technology: New Generations. ITNG ’11, Washington, DC, USA, IEEE Computer Society, 2011, pp. 441–446.
  6. Akshata. S. (2016) “Eye Blink Detection Using Adaboost Approach and Morphological Operation” In: International journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. Vol 5, Issue 4, April 2016.
  7. Dominic Asamoah, Peter Amoako-Yirenkyi, Stephen Opoku Oppong and Nuku Atta KordzoAbiew(2017) “Establishing the Blink Cycle of the Eye using OTSU Method and Gaussian Filter” In: International Journal of Computer Applications (0975 – 8887) Volume 175 – No.4, October 2017
  8. X. Wang and X. Tang (2009) “Face Photo-Sketch Synthesis and Recognition” In: IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 31, 2009.
  9. Jian Yang, David Zhang, Alejandro F. Frangi, and Jing-yu Yang (2004) “Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition” In: Journal IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 26 Issue 1, January 2004 Page 131-137
  10. Taigman, Y. (2014) “Deepface: Closing the gap to human-level performance in face verification.”In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014
  11. Benuwa, B.B (2016) “A Review of Deep Machine Learning.”In International Journal of Engineering Research in Africa. 2016. Trans Tech Publ.
  12. Robert Yao Aaronson, Wu Chen and Ben-Bright Benuwa. (2017) “Robust Face Detection using Convolutional Neural Network.” In: International Journal of Computer Applications 170(6):14-20, July 2017.
  13. VijayalakshmiA. (2017) “Recognizing Faces with Partial Occlusion using in painting.” International Journal of Computer Applications 168(13):20-24, June 2017
Index Terms

Computer Science
Information Sciences

Keywords

Open Face framework linear SVM classifier face recognition model OpenCV detect eye-blinks eye aspect ratio Neural Networks CNN