Öz
Facial recognition is used efficiently in human-computer interactions, passports, driver’s licence, border controls, video surveillance and criminal identification, and is an important biometric’s security option in many device-related security requirements. In this paper, we use Eigenface recognition based on the Principal Component Analysis (PCA) to develop the project. PCA aims to reduce the size of large image matrices and is used for feature extraction. Then, we use the euclidean distance method for classification. The dataset used in this project was obtained by AT&T Laboratories at Cambridge University [1]. The training dataset contains grayscale facial images of 40 people; each person has 10 different facial images taken from different angles and emotions.
This study aims to give researchers a hunch before they start to develop image recognition using deep learning methods. It also shows that face recognition can be done without deep learning.
Anahtar Kelimeler
Eigenface PCA Classification Facial Recognition Distance Methods
Kaynakça
- “The database of faces, [accessed 7 dec. 2022].” [Online]. Available: https://cam.orl.co.uk/facedatabase.html
- C. E. Bugge, J. Burkhardt, K. S. Dugstad, T. B. Enger, M. Kasprzycka, A. Kleinauskas, M. Myhre, K. Scheffler, S. Ström, and S. Vetlesen, “Biometric methods of animal identification,” Course notes, Laboratory Animal Science at the Norwegian School of Veterinary Science, pp. 1–6, 2011.
- S. Prabhakar, A. K. Jain, and S. Pankanti, “Learning fingerprint minutiae location and type,” Pattern Recognition, vol. 36, no. 8, pp. 1847–1857, 2003.
- H. Mehrotra, M. Vatsa, R. Singh, and B. M, “Does iris change over time?” PloS one, vol. 8, no. 11, p. e78333, 2013.
- H. Mehrotra, M. Vatsa, R. Singh, and B. Majhi, “Does iris change over time?” PloS one, vol. 8, no. 11, p. e78333, 2013.
- K. S. P. Reddy and D. K. N. Raju, “Design and implementation of an algorithm for face recognition by using principal component analysis (pca) in matlab,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 6, no. 10, pp. 115–119, 2016.
- N. Abouzakhar and P. Enjamuri, “An enhanced eigenfaces-based biometric forensic model,” in Procs 4th International Conference on Cybercrime Forensics Education & Training, 2010.
- S. Satonkar Suhas, B. Kurhe Ajay, and B. Prakash Khanale, “Face recognition using principal component analysis and linear discriminant analysis on holistic approach in facial images database,” Int Organ Sci Res, vol. 2, no. 12, pp. 15–23, 2012.
- M. Karnan, “Face recognition using multiple eigenface subspaces,” Journal of Engineering and Technology Research, vol. 2, no. 8, pp. 139–143, 2010.
- A. Kaur and T. Sarabjit Singh, “Face recognition using pca (principal component analysis) and lda (linear discriminant analysis) techniques,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 3, pp. 308–310, 2015.
- M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71–86, 1991.
- M. M. Ahsan, Y. Li, J. Zhang, M. T. Ahad, and K. D. Gupta, “Evaluating the performance of eigenface, fisherface, and local binary pattern histogram-based facial recognition methods under various weather conditions,” Technologies, vol. 9, no. 2, p. 31, 2021.
- S. Gupta, O. Sahoo, A. Goel, and R. Gupta, “A new optimized approach to face recognition using eigenfaces,” Global Journal of Computer Science and Technology, 2010.
- A. Ghosh, P. K. Kundu, and G. Sarkar, “Similarity detection of illuminance images using eigenface method,” Journal of The Institution of Engineers (India): Series B, pp. 1–5, 2022.
- M. Slavković and D. Jevtić, “Face recognition using eigenface approach,” Serbian Journal of electrical engineering, vol. 9, no. 1, pp. 121–130, 2012.
- M. Imran, M. Miah, H. Rahman, A. Bhowmik, and D. Karmaker, “Face recognition using eigenfaces,” International Journal of Computer Applications, vol. 118, no. 5, 2015.
Öz
Kaynakça
- “The database of faces, [accessed 7 dec. 2022].” [Online]. Available: https://cam.orl.co.uk/facedatabase.html
- C. E. Bugge, J. Burkhardt, K. S. Dugstad, T. B. Enger, M. Kasprzycka, A. Kleinauskas, M. Myhre, K. Scheffler, S. Ström, and S. Vetlesen, “Biometric methods of animal identification,” Course notes, Laboratory Animal Science at the Norwegian School of Veterinary Science, pp. 1–6, 2011.
- S. Prabhakar, A. K. Jain, and S. Pankanti, “Learning fingerprint minutiae location and type,” Pattern Recognition, vol. 36, no. 8, pp. 1847–1857, 2003.
- H. Mehrotra, M. Vatsa, R. Singh, and B. M, “Does iris change over time?” PloS one, vol. 8, no. 11, p. e78333, 2013.
- H. Mehrotra, M. Vatsa, R. Singh, and B. Majhi, “Does iris change over time?” PloS one, vol. 8, no. 11, p. e78333, 2013.
- K. S. P. Reddy and D. K. N. Raju, “Design and implementation of an algorithm for face recognition by using principal component analysis (pca) in matlab,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 6, no. 10, pp. 115–119, 2016.
- N. Abouzakhar and P. Enjamuri, “An enhanced eigenfaces-based biometric forensic model,” in Procs 4th International Conference on Cybercrime Forensics Education & Training, 2010.
- S. Satonkar Suhas, B. Kurhe Ajay, and B. Prakash Khanale, “Face recognition using principal component analysis and linear discriminant analysis on holistic approach in facial images database,” Int Organ Sci Res, vol. 2, no. 12, pp. 15–23, 2012.
- M. Karnan, “Face recognition using multiple eigenface subspaces,” Journal of Engineering and Technology Research, vol. 2, no. 8, pp. 139–143, 2010.
- A. Kaur and T. Sarabjit Singh, “Face recognition using pca (principal component analysis) and lda (linear discriminant analysis) techniques,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 3, pp. 308–310, 2015.
- M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71–86, 1991.
- M. M. Ahsan, Y. Li, J. Zhang, M. T. Ahad, and K. D. Gupta, “Evaluating the performance of eigenface, fisherface, and local binary pattern histogram-based facial recognition methods under various weather conditions,” Technologies, vol. 9, no. 2, p. 31, 2021.
- S. Gupta, O. Sahoo, A. Goel, and R. Gupta, “A new optimized approach to face recognition using eigenfaces,” Global Journal of Computer Science and Technology, 2010.
- A. Ghosh, P. K. Kundu, and G. Sarkar, “Similarity detection of illuminance images using eigenface method,” Journal of The Institution of Engineers (India): Series B, pp. 1–5, 2022.
- M. Slavković and D. Jevtić, “Face recognition using eigenface approach,” Serbian Journal of electrical engineering, vol. 9, no. 1, pp. 121–130, 2012.
- M. Imran, M. Miah, H. Rahman, A. Bhowmik, and D. Karmaker, “Face recognition using eigenfaces,” International Journal of Computer Applications, vol. 118, no. 5, 2015.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Bilgisayar Görüşü ve Çoklu Ortam Hesaplama (Diğer), Yapay Zeka |
| Bölüm | Research Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 28 Aralık 2022 |
| Gönderilme Tarihi | 12 Aralık 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 3 Sayı: 2 |
Kaynak Göster
This work is licensed under a Creative Commons Attribution 4.0 International License.