Face Recognition and Gender Detection Using SIFT Feature Extraction, LBPH, and SVM
Received: 4 January 2022 | Revised: 31 January 2022 | Accepted: 4 February 2022 | Online: 9 April 2022
Corresponding author: M. Alghamdi
Abstract
Face recognition and name and gender identification are challenging processes, especially when identifying perpetrators and suspects or when used in authentication systems. Machine learning and computer vision technologies are used in many fields, including security, and play an important role in face recognition and gender detection, offering valuable information to officials to rectify a situation in less time. This study used a few machine learning methods in the Labelled Faces in the Wild (LFW) database to examine their facial recognition and gender detection capacities. The LFW dataset was used to train and evaluate the Scale Invariant Feature Transform (SIFT) feature extraction method along with the Support Vector Machine (SVM) classifier and the Local Binary Pattern Histogram (LBPH) method. The result comparison from the current and other studies showed that the proposed LBPH method had higher accuracy in face recognition, while its accuracy in gender detection was very close to the ones of other, relevant studies.
Keywords:
face recognation, gender predection, SVM, SIFT, LBPHDownloads
References
R. Baly, A. Khaddaj, H. Hajj, W. El-Hajj, and K. B. Shaban, "ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets," in Proceedings of the LREC 2018 Workshop "The 3rd Workshop on Open-Source Arabic Corpora and Processing Tools," May 2019, pp. 37–43.
T. Song, K. Xiang, and X.-Y. Wang, "Face recognition under varying illumination based on gradientface and local features," IEEJ Transactions on Electrical and Electronic Engineering, vol. 10, no. 2, pp. 222–228, 2015. DOI: https://doi.org/10.1002/tee.22057
S. Kalam and G. Guttikonda, "Gender Classification using Geometric Facial Features," International Journal of Computer Applications, vol. 85, no. 7, pp. 32–37, Jan. 2014. DOI: https://doi.org/10.5120/14855-3222
S. K. Kim, Y. J. Park, K. A. Toh, and S. Lee, "SVM-based feature extraction for face recognition," Pattern Recognition, vol. 43, no. 8, pp. 2871–2881, May 2010. DOI: https://doi.org/10.1016/j.patcog.2010.03.008
T. Ahonen, A. Hadid, and M. Pietikäinen, "Face Recognition with Local Binary Patterns," in Computer Vision - ECCV 2004, Prague, Czech Republic, 2004, pp. 469–481. DOI: https://doi.org/10.1007/978-3-540-24670-1_36
Z. Cao, Q. Yin, X. Tang, and J. Sun, "Face recognition with learning-based descriptor," in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, Jun. 2010, pp. 2707–2714. DOI: https://doi.org/10.1109/CVPR.2010.5539992
Q. Yin, X. Tang, and J. Sun, "An associate-predict model for face recognition," in CVPR 2011, Colorado Springs, CO, USA, Jun. 2011, pp. 497–504. DOI: https://doi.org/10.1109/CVPR.2011.5995494
Y. Duan, J. Lu, and J. Zhou, "UniformFace: Learning Deep Equidistributed Representation for Face Recognition," in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, Jun. 2019, pp. 3410–3419. DOI: https://doi.org/10.1109/CVPR.2019.00353
V. A, D. Hebbar, V. S. Shekhar, K. N. B. Murthy, and S. Natarajan, "Two Novel Detector-Descriptor Based Approaches for Face Recognition Using SIFT and SURF," Procedia Computer Science, vol. 70, pp. 185–197, Jan. 2015. DOI: https://doi.org/10.1016/j.procs.2015.10.070
A. Ahmed, J. Guo, F. Ali, F. Deeba, and A. Ahmed, "LBPH based improved face recognition at low resolution," in 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, Feb. 2018, pp. 144–147. DOI: https://doi.org/10.1109/ICAIBD.2018.8396183
A. Rattani, N. Reddy, and R. Derakhshani, "Gender prediction from mobile ocular images: A feasibility study," in 2017 IEEE International Symposium on Technologies for Homeland Security (HST), Waltham, MA, USA, Apr. 2017, pp. 1–6. DOI: https://doi.org/10.1109/THS.2017.7943489
U. U. Tariq, W. Ahmad, M. D. A. Asif, and Hussain, Mubahir, "Gender Perception From Faces Using Boosted LBPH (Local Binary Pattern Histograms)," Carpathian Journal of Electronic and Computer Engineering, vol. 6, no. 1, pp. 8–15, 2013.
D. Virmani, P. Girdhar, P. Jain, and P. Bamdev, "FDREnet: Face Detection and Recognition Pipeline," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3933–3938, Apr. 2019. DOI: https://doi.org/10.48084/etasr.2492
Y. Said, M. Barr, and H. E. Ahmed, "Design of a Face Recognition System based on Convolutional Neural Network (CNN)," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5608–5612, Jun. 2020. DOI: https://doi.org/10.48084/etasr.3490
F.-C. Huang, S.-Y. Huang, J.-W. Ker, and Y.-C. Chen, "High-Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction," IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 3, pp. 340–351, Mar. 2012. DOI: https://doi.org/10.1109/TCSVT.2011.2162760
P. Matlani and M. Shrivastava, "An Efficient Algorithm Proposed For Smoke Detection in Video Using Hybrid Feature Selection Techniques," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3939–3944, Apr. 2019. DOI: https://doi.org/10.48084/etasr.2571
C. Geng and X. Jiang, "Face recognition using sift features," in 2009 16th IEEE International Conference on Image Processing (ICIP), Aug. 2009, pp. 3313–3316.
N. Stekas and D. Van Den Heuvel, "Face Recognition Using Local Binary Patterns Histograms (LBPH) on an FPGA-Based System on Chip (SoC)," in 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Chicago, IL, USA, Feb. 2016, pp. 300–304. DOI: https://doi.org/10.1109/IPDPSW.2016.67
A. D. Gadekar and S. S. Suresh, "Face Recognition Using SIFT- PCA Feature Extraction and SVM Classifier," IOSR Journal of VLSI and Signal Processing, vol. 5, no. 2, pp. 31–35, Apr. 2015.
P. J. Phillips, "Support vector machines applied to face recognition," in Proceedings of the 11th International Conference on Neural Information Processing Systems, Cambridge, MA, USA, Sep. 1998, pp. 803–809. DOI: https://doi.org/10.6028/NIST.IR.6241
T. Kluyver et al., "Jupyter Notebooks – a publishing format for reproducible computational workflows," Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 87–90, 2016.
J. Reback et al., pandas-dev/pandas: Pandas 1.3.4. Zenodo, 2021.
L. Buitinck et al., "API design for machine learning software: experiences from the scikit-learn project," arXiv:1309.0238 [cs], Sep. 2013.
University of Massachusetts, "LFW Face Database - Labeled Faces in the Wild." http://vis-www.cs.umass.edu/lfw/.
P. Viola and M. J. Jones, "Robust Real-Time Face Detection," International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, Feb. 2004. DOI: https://doi.org/10.1023/B:VISI.0000013087.49260.fb
J. Brownlee, "Machine Learning Mastery," Machine Learning Mastery. https://machinelearningmastery.com/ (accessed Feb. 11, 2022).
Downloads
How to Cite
License
Copyright (c) 2022 H. Alamri, E. Alshanbari, S. Alotaibi, M. Alghamdi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
