Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence
Received: 2 February 2024 | Revised: 11 February 2024 and 16 February 2024 | Accepted: 19 February 2024 | Online: 2 April 2024
Corresponding author: Hind Moutaz Al-Dabbas
Abstract
In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimensional Convolutional Neural Network Hybrid Model (1D-CNNHM). The MUCT database was considered for training and evaluation. The performance, in terms of classification, of the J48 model reached 96.01% accuracy whereas the DL model that merged LDA with MI and ANOVA reached 100% accuracy. Comparing the proposed models with other works reflects that they are performing very well, with high accuracy and low processing time.
Keywords:
ANOVA, CNN, face recognition, DLA, MIDownloads
References
A. Y.Noori, S. H. Shaker, and R. A. Azeez, "3D scenes semantic segmentation using deep learning based Survey," IOP Conference Series: Materials Science and Engineering, vol. 928, no. 3, Aug. 2020, Art. no. 032083.
A. Azeez and Raghad, "Determination Efficient Classification Algorithm for Credit Card Owners: Comparative Study," Engineering and Technology Journal, vol. 39, no. 1B, pp. 21–29, Mar. 2021.
N. H. Ali, M. E. Abdulmunem, and A. E. Ali, "Constructed model for micro-content recognition in lip reading based deep learning," Bulletin of Electrical Engineering and Informatics, vol. 10, no. 5, pp. 2557–2565, Oct. 2021.
W. Najah Abdullah and Y. Hussain Ali, "Face Retrieval Using Image Moments and Genetic Algorithm," Engineering and Technology Journal, vol. 34, no. 1B, pp. 160–171, Jan. 2016.
H. Al-Dabbas and F. Mohammed, "The Effect of Wavelet Coefficient Reduction on Image Compression Using DWT and Daubechies Wavelet Transform," Science International, vol. 30, no. 5, pp. 757–762, Jan. 2018.
N. A. Taha, Z. Qasim, A. Al-Saffar, and A. A. Abdullatif, "Steganography using dual tree complex wavelet transform with LSB indicator technique," Periodicals of Engineering and Natural Sciences, vol. 9, no. 2, pp. 1106–1114, Jun. 2021.
W. N. Abdullah, "Solving Job-Shop Scheduling Problem Using a Developed Particle Swarm Optimization Algorithm," International Journal of Science and Research, vol. 7, no. 1, pp. 1845–1848, 2015.
F. B. Ibrahim and M. E. Abdulmunim, "Real Time Face Recognition System based Hybrid Method," International Journal of Scientific Research & Management Studies, vol. 4, no. 4, pp. 6–11, May 2018.
A. H. Morad and H. M. Al-Dabbas, "Classification of Brain Tumor Area for MRI images," Journal of Physics: Conference Series, vol. 1660, no. 1, Aug. 2020, Art. no. 012059.
N. H. Barnouti, S. S. M. Al-Dabbagh, W. E. Matti, and M. A. S. Naser, "Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance," International Journal of Advanced Computer Science and Applications, vol. 7, no. 5, pp. 371–377, 2016.
J. Zhou and B. Zhang, "Collaborative Representation Using Non-Negative Samples for Image Classification," Sensors, vol. 19, no. 11, Jan. 2019, Art. no. 2609.
J. A. C. Moreano and N. B. L. S. Palomino, "Efficient Technique for Facial Image Recognition With Support Vector Machines in 2D Images With Cross-validation in Matlab," Wseas Transactions on Systems and Control, vol. 15, pp. 175–183, May 2020.
A. H. Rashed and M. H. Hamd, "Robust Detection and Recognition System based on Facial Extraction and Decision Tree," Journal of Engineering and Sustainable Development, vol. 25, no. 4, pp. 40–50, Jul. 2021.
R. Szmurlo and S. Osowski, "Ensemble of classifiers based on CNN for increasing generalization ability in face image recognition," Bulletin of the Polish Academy of Sciences. Technical Sciences, vol. 70, no. 3, 2022, Art. no. e141004.
E. M. Silva, M. Boaventura, I. A. G. Boaventura, and R. C. Contreras, "Face Recognition Using Local Mapped Pattern and Genetic Algorithms," in International Conference on Pattern Recognition and Artificial Intelligence, Union, NJ, USA, Dec. 2018, pp. 11–17.
E. Setiawan and A. Muttaqin, "Implementation of K-Nearest Neightbors Face Recognition on Low-power Processor," Telkomnika, vol. 13, no. 3, pp. 949–954, Sep. 2015.
C. Szegedy et al., "Going deeper with convolutions," in IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, Jun. 2015, pp. 1–9.
I. Alhakam and N. H. Salman, "An Improved Probability Density Function (PDF) for Face Skin Detection," Iraqi Journal of Science, vol. 63, no. 10, pp. 4460–4473, Oct. 2022.
S. O. Adeshina, H. Ibrahim, S. S. Teoh, and S. C. Hoo, "Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons," Electronics, vol. 10, no. 2, Jan. 2021, Art. no. 102.
N. K. Benamara, M. Keche, M. Wellington, and Z. Munyaradzi, "Securing E-payment Systems by RFID and Deep Facial Biometry," in 1st International Conference on Artificial Intelligence and Data Analytics, Riyadh, Saudi Arabia, Apr. 2021, pp. 151–157.
W. Zhang and C. Zhao, "Exposing Face-Swap Images Based on Deep Learning and ELA Detection," Proceedings, vol. 46, no. 1, 2019, Art. no. 29.
A. Al-Qaisi, M. Altarawneh, A. ElSaid, and Z. Al Qadi, "A Hybrid Method of Face Feature Extraction, Classification Based on MLBP and Layered- Recurrent Network," Traitement du Signal, vol. 37, pp. 555–561, Oct. 2020.
H. M. Al-Dabbas, R. A. Azeez, and A. E. Ali, "Machine Learning Approach for Facial Image Detection System," Iraqi Journal of Science, vol. 64, no. 10, pp. 6328–6341, Oct. 2023.
"The MUCT Face Database." http://www.milbo.org/muct/.
F. G. Mohammed and H. M. Al-Dabbas, "Application of WDR Technique with different Wavelet Codecs for Image Compression," Iraqi Journal of Science, vol. 59, no. 4B, pp. 2128–2134, Nov. 2018.
A. Salim Jamil, R. Azeez, and N. Hassan, "An Image Feature Extraction to Generate a Key for Encryption in Cyber Security Medical Environments," International Journal of Online and Biomedical Engineering, vol. 19, no. 1, pp. 93–106, Jan. 2023.
J. M. AL-Tuwaijari, S. I. Mohammed, and M. A. B. Rahem, "Performance Evaluation of Face Image Recognition Based Voila–Joins with SVM," Iraqi Journal of Information Technology, vol. 9, no. 1, pp. 48–64, 2018.
H. Alamri, E. Alshanbari, S. Alotaibi, and M. Alghamdi, "Face Recognition and Gender Detection Using SIFT Feature Extraction, LBPH, and SVM," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8296–8299, Apr. 2022.
M. N. Chaudhari, M. Deshmukh, G. Ramrakhiani, and R. Parvatikar, "Face Detection Using Viola Jones Algorithm and Neural Networks," in Fourth International Conference on Computing Communication Control and Automation, Pune, India, Aug. 2018, pp. 1–6.
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.
M. K. Dabhi and B. K. Pancholi, "Face Detection System Based on Viola - Jones Algorithm," International Journal of Science and Research, vol. 5, no. 4, pp. 62–64, 2013.
M. E. Abdulmunem and F. B. Ibrahim, "Design of an Efficient Face Recognition Algorithm based on Hybrid Method of Eigen Faces and Gabor Filter," Iraqi Journal of Science, vol. 57, no. 3B, pp. 2102–2110, 2016.
H. Santoso and R. Pulungan, "A Parallel Architecture for Multiple-Face Detection Technique Using AdaBoost Algorithm and Haar Cascade," in Information Systems International Conference, Bali, Indonesia, Dec. 2013, pp. 592–597.
N. Singhal, V. Ganganwar, M. Yadav, A. Chauhan, M. Jakhar, and K. Sharma, "Comparative study of machine learning and deep learning algorithm for face recognition," Jordanian Journal of Computers and Information Technology, vol. 7, no. 3, pp. 313–325, Sep. 2021.
Y. Aliyari Ghassabeh, F. Rudzicz, and H. A. Moghaddam, "Fast incremental LDA feature extraction," Pattern Recognition, vol. 48, no. 6, pp. 1999–2012, Jun. 2015.
E. A. Khorsheed and Z. A. Nayef, "Face Recognition Algorithms: A Review," Academic Journal of Nawroz University, vol. 11, no. 3, pp. 202–207, Aug. 2022.
H. M. Al-Dabbas, R. A. Azeez, and A. E. Ali, "Digital Watermarking, Methodology, Techniques, and Attacks: A Review," Iraqi Journal of Science, vol. 64, no. 8, pp. 4169–4186, Aug. 2023.
K. J. Johnson and R. E. Synovec, "Pattern recognition of jet fuels: comprehensive GC×GC with ANOVA-based feature selection and principal component analysis," Chemometrics and Intelligent Laboratory Systems, vol. 60, no. 1, pp. 225–237, Jan. 2002.
S. Shakeela, N. S. Shankar, P. M. Reddy, T. K. Tulasi, and M. M. Koneru, "Optimal Ensemble Learning Based on Distinctive Feature Selection by Univariate ANOVA-F Statistics for IDS," International Journal of Electronics and Telecommunications, no. Vol. 67, No. 2, pp. 267–275, 2021.
Y. H. Ali and W. N. Abdullah, "A Survey of Similarity Measures in Web Image Search," International Journal of Emerging Trends & Technology in Computer Science, vol. 4, no. 4, pp. 191–196, 2015.
H. M. Al-Dabbas, R. A. Azeez, and A. E. Ali, "Efficient Iris Image Recognition System Based on Machine Learning Approach," Iraqi Journal of Computers, Communications, Control & Systems Engineering, vol. 23, no. 3, pp. 104–114, 2023.
T. Saidani, "Deep Learning Approach: YOLOv5-based Custom Object Detection," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12158–12163, Dec. 2023.
R. Rajamohanan and B. C. Latha, "An Optimized YOLO v5 Model for Tomato Leaf Disease Classification with Field Dataset," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12033–12038, Dec. 2023.
E. R. V. Reddy and S. Thale, "A Novel Efficient Dual-Gate Mixed Dilated Convolution Network for Multi-Scale Pedestrian Detection," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 11973–11979, Dec. 2023.
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Copyright (c) 2024 Hind Moutaz Al-Dabbas, Raghad Abdulaali Azeez, Akbas Ezaldeen Ali
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