Araştırma Makalesi
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Performance comparison of PCA and ICA algorithms-based face recognition system

Yıl 2021, Cilt: 13 Sayı: 3, 103 - 120, 31.12.2021

Öz

Face recognition system has become a dominant research area in biometric studies due to its efficiency and accuracy. This technology has been broadly invested in various security applications for the automatic identification of humans. However, the complexity of human faces representation with the large variation in its characteristics and appearances. This complexity involves adopting powerful algorithms that can effectively learn and overcome such problems with less false results. Many algorithms are proposed for this purpose such as the Principal Component Analysis (PCA) and the Independent Components Analysis (ICA), etc. This work focuses on the implementation of a reliable face recognition system using PCA and ICA as recognition methods and the Euclidean Distance (ED) as a face classifier. A comparison is conducted upon the performances of the PCA and the ICA. These two methods are mainly used in this research for image projection and dimensionality reduction. The classification process is performed by using the distance measure scheme that is adopted by the ED classifier. The comparison is taken for the system robustness evaluation in terms of recognizing a given set of face images.

Destekleyen Kurum

University of Bedfordshire

Teşekkür

Thanks to all researchers who have commented on this

Kaynakça

  • [1] B. Rios-Sanchez, M. Viana-Matesanz, and C. Sanchez-Avila, “A comparative study of palmprint feature extraction methods for contact-less biometrics under different environmental conditions,” 2017 International Carnahan Conference on Security Technology (ICCST), Oct. 2017.
  • [2] G. Ragul, C. MageshKumar, R. Thiyagarajan, and R. Mohan, “Comparative study of statistical models and classifiers in face recognition,” 2013 International Conference on Information Communication and Embedded Systems (ICICES), Feb. 2013.
  • [3] M. M. Sani, K. A. Ishak, and S. A. Samad, “Evaluation of face recognition system using Support Vector Machine,” 2009 IEEE Student Conference on Research and Development (SCOReD), 2009.
  • [4] X. Li and G. Chen, “Face Recognition Based on PCA and SVM,” 2012 Symposium on Photonics and Optoelectronics, May. 2012.
  • [5] Y. Wu, Q. Nian, and S. Gu, “An improved Learning Evaluation system based on SVM for E-learning,” 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), Oct. 2012.
  • [6] T. Kaleekal and J. Singh, “Facial Expression recognition using higher order moments on facial patches,” 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Jul. 2019.
  • [7] W. Zhao, R. Chellappa, P.J. Philips, A. Rosenfeld, “Face recognition: A literature survey,” 2003Acm Computing Surveys (CSUR), Dec. 2003.
  • [8] F. Ahmad, A. Najam, and Z. Ahmed, “Image-based Face Detection and Recognition :"State of the Art ",’” Int. J. Comput. Sci. Issues, pp. 3–6, 2012.
  • [9] Chenggang Zhen and Yingmei Su, “Research about human face recognition technology,” 2009 International Conference on Test and Measurement, Dec. 2009.
  • [10] M. Nie, Y. Li, J. Zhang, and S. Wang, “The facial features analysis method based on human star-structured model,” 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE), Sep. 2019.
  • [11] E. Naz, U. Farooq, and T. Naz, “Analysis of Principal Component Analysis-Based and Fisher Discriminant Analysis-Based Face Recognition Algorithms,” 2006 International Conference on Emerging Technologies, 2006.
  • [12] M. A. Lone, S. M. Zakariya, and R. Ali, “Automatic Face Recognition System by Combining Four Individual Algorithms,” 2011 International Conference on Computational Intelligence and Communication Networks, Oct. 2011.
  • [13] H. Chen and J. Lu, “The image process technologies in face recognition,” The 2nd International Conference on Information Science and Engineering, Dec. 2010.
  • [14] S. Kar, S. Hiremath, D. G. Joshi, V. K. Chadda, and A. Bajpai, “A Multi-Algorithmic Face Recognition System,” 2006 International Conference on Advanced Computing and Communications, Dec. 2006. [15] I. M. Al-Bahri, S. O. Fageeri, A. M. Said, and G. M. A. Sagayee, “A Comparative Study Between PCA and Sift Algorithm for Static Face Recognition,” 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Feb. 2021.
  • [16] S. K. Dandpat and S. Meher, “Performance improvement for face recognition using PCA and two-dimensional PCA,” 2013 International Conference on Computer Communication and Informatics, Jan. 2013.
  • [17] L. Zunxiong, Z. Lihui, and Z. Heng, “Appearance-Based Subspace Projection Techniques for Face Recognition,” 2009 International Asia Symposium on Intelligent Interaction and Affective Computing, Dec. 2009.
  • [18] M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
  • [19] R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and machine recognition of faces: a survey,” Proceedings of the IEEE, vol. 83, no. 5, pp. 705–741, May 1995.
  • [20] S. Gushanskiy and V. Potapov, “Investigation of Quantum Algorithms for Face Detection and Recognition Using a Quantum Neural Network,” 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), May 2021.
  • [21] W. Chen, T. Sun, X. Yang, and L. Wang, “Face detection based on half face-template,” 2009 9th International Conference on Electronic Measurement & Instruments, Aug. 2009.
  • [22] S. K. Pakazad, F. Hajati, and S. D. Farahani, “A Face Detection Framework based on Selected Face Components,” Journal of Applied Sciences, vol. 11, no. 2, pp. 247–256, Jan. 2011.
  • [23] M. A. A. Akash, M. A. H. Akhand, and N. Siddique, “Robust Face Detection Using Hybrid Skin Color Matching under Different Illuminations,” 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Feb. 2019.
  • [24] A. Majumder, L. Behera, and V. K. Subramanian, “Automatic and Robust Detection of Facial Features in Frontal Face Images,” 2011 UkSim 13th International Conference on Computer Modelling and Simulation, Mar. 2011.
  • [25] A. A. Khair, Z. Zainuddin, A. Achmad, and A. A. Ilham, “Face Recognition in Kindergarten Students using the Principal Component Analysis Algorithm,” 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), Oct. 2019.
  • [26] Weilin Huang and H. Yin, “Linear and nonlinear dimensionality reduction for face recognition,” 2009 16th IEEE International Conference on Image Processing (ICIP), Nov. 2009.
  • [27] Shi-Fei Ding, Zhong-Zhi Shi, Yong Liang, and Feng-Xiang Jin, “Information feature analysis and improved algorithm of PCA,” 2005 International Conference on Machine Learning and Cybernetics, 2005.
  • [28] S. Z. Li and Juwei Lu, “Generalizing capacity of face database for face recognition,” Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998.
  • [29] L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” Journal of the Optical Society of America A, vol. 4, no. 3, p. 519, Mar. 1987. [30] A. Anand, M. A. Haque, J. S. R. Alex, and N. Venkatesan, “Evaluation of Machine learning and Deep learning algorithms combined with dimensionality reduction techniques for classification of Parkinson’s Disease,” 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Dec. 2018.
  • [31] Neerja and E. Walia, “Face Recognition Using Improved Fast PCA Algorithm,” 2008 Congress on Image and Signal Processing, 2008.
  • [32] S. Nedevschi, I. R. Peter, and A. Mandrut, “PCA type algorithm applied in face recognition,” 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, Aug. 2012.
  • [33] Lin Luo, M. N. S. Swamy, and E. I. Plotkin, “A modified PCA algorithm for face recognition,” CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).
  • [34] C. Tosik, A. Eleyan, and M. S. Salman, “Illumination invariant face recognition system,” 2013 21st Signal Processing and Communications Applications Conference (SIU), Apr. 2013.
  • [35] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Transactions on Neural Networks, vol. 13, no. 6, pp. 1450–1464, Nov. 2002.
  • [36] A. J. Goldstein, L. D. Harmon, and A. B. Lesk, “Identification of human faces,” Proceedings of the IEEE, vol. 59, no. 5, pp. 748–760, 1971.
  • [37] H. Deng, Z. Feng, Y. Liu, D. Luo, X. Yang, and H. Li, “Face Recognition Algorithm Based on Weighted Intensity PCNN,” 2020 Eighth International Conference on Advanced Cloud and Big Data (CBD), Dec. 2020.
  • [38] I. Buciu, C. kotropoulos, and I. Pitas, “ICA and Gabor representation for facial expression recognition,” Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
  • [39] M. Srinivasan and V. Aravamudhan, “Independent Component Analysis of Edge Information for Face Recognition under Variation of Pose and Illumination,” 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation, Sep. 2012.
  • [40] Y. Zhou, S. Cao, D. Wen, H. Zhang, and L. Zhao, “The study of face recognition based on hybrid principal components analysis and independent component analysis,” 2011 International Conference on Electronics, Communications and Control (ICECC), Sep. 2011.
  • [41] Y. Wang, H. Li, and Y. Guo, “Face recognition based on ICA and SPSO-ELM,” 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Dec. 2017.
  • [42] JingHua Cao, YanZhong Ran, and ZhiJun Xu, “A new Kernel function based face recognition algorithm,” 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, Aug. 2010.
  • [43] Jian Yang, D. Zhang, and Jing-Yu Yang, “Constructing PCA Baseline Algorithms to Re-evaluate ICA-Based Face-Recognition Performance,” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 37, no. 4, pp. 1015–1021, Aug. 2007.
  • [44] J. Yang, X. Gao, D. Zhang, and J. Yang, “Kernel ICA: An alternative formulation and its application to face recognition,” Pattern Recognition, vol. 38, no. 10, pp. 1784–1787, Oct. 2005.
  • [45] N. T. N. Babu, A. A. Fathima, and V. Vaidehi, “An Efficient Face Recognition System Using DWT-ICA Features,” 2011 International Conference on Digital Image Computing: Techniques and Applications, Dec. 2011.
  • [46] M. Luo, L. Song, and S. Li, “An Improved Face Recognition Based on ICA and WT,” 2012 IEEE Asia-Pacific Services Computing Conference, Dec. 2012.
  • [47] Y. Huang, M. Li, C. Lin, and L. Tian, “Gabor-Based Kernel Independent Component Analysis for Face Recognition,” 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Oct. 2010.
  • [48] B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with PCA and ICA,” Computer Vision and Image Understanding, vol. 91, no. 1–2, pp. 115–137, Jul. 2003.
  • [49] R. Thiyagarajan, S. Arulselvi, and G. Sainarayanan, “Gabor feature based classification using statistical models for face recognition,” Procedia Computer Science, vol. 2, pp. 83–93, 2010.
  • [50] Liwei Wang, Yan Zhang, and Jufu Feng, “On the Euclidean distance of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1334–1339, Aug. 2005.
  • [51] G. Thilagavathi and M. Suriakala, “Survey on face recognition,” 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), Apr. 2021.
  • [52] C. Chen and J. Tang, “Simulation study on the performance of several classifiers in face recognition,” 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, May 2012.
  • [53] D. Jelsovka, R. Hudec, and M. Breznan, “Face recognition on FERET face database using LDA and CCA methods,” 2011 34th International Conference on Telecommunications and Signal Processing (TSP), Aug. 2011.
  • [54] M. Grgic, K. Delac,2013. Face Recognition Homepage.[Online]. [Accessed 17 May 2021]. Available from: http://www.face-rec.org/databases/
  • [55] W. A. Barrett, “A survey of face recognition algorithms and testing results,” Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).
  • [56] Z. Lihong, W. Ye, and T. Hongfeng, “Face recognition based on independent component analysis,” 2011 Chinese Control and Decision Conference (CCDC), May 2011.
  • [57] Aalto University, Finlad, Department of Information and Computer Science, 2013. Independent Component Analysis (ICA) and Blind Source Separation (BSS). [Online].[Accessed 20 November 2013]. Available from: http://research.ics.aalto.fi/ica/fastica/
  • [58] S. Bag and G. Sanyal, “An efficient face recognition approach using PCA and minimum distance classifier,” 2011 International Conference on Image Information Processing, Nov. 2011.
  • [59] M. Imran, S. Noushath, A. Abdesselam, K. Jetly, and K. Karthikeyan, “Efficient multi-algorithmic approaches for face recognition using subspace methods,” 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), Feb. 2013.
  • [60] J.-H. Chen, C.-L. Lai, C.-H. Chu, and J.-Y. Hsu, “Improving accuracy of facial recognition systems for distant targets,” 2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE), Oct. 2013.

PCA ve ICA algoritmaları tabanlı yüz tanıma sisteminin performans karşılaştırması

Yıl 2021, Cilt: 13 Sayı: 3, 103 - 120, 31.12.2021

Öz

Yüz tanıma sistemi, verimliliği ve doğruluğu nedeniyle biyometrik çalışmalarda baskın bir araştırma alanı haline gelmiştir. Bu teknoloji, insanların otomatik olarak tanımlanması için çeşitli güvenlik uygulamalarına geniş ölçüde yatırım edilmiştir.Bununla birlikte, insanın karmaşıklığı, özellikleri ve görünümlerindeki büyük varyasyonla temsille karşı karşıyadır. Bu karmaşıklık, bu tür sorunları daha az yanlış sonuçlarla etkili bir şekilde öğrenebilen ve üstesinden gelebilen güçlü algoritmaları benimsemeyi içerir. Bu amaçla Temel Bileşen Analizi (PCA) ve Bağımsız Bileşen Analizi (ICA) vb. Bu çalışma, tanıma yöntemleri olarak PCA ve ICA kullanılarak güvenilir bir yüz tanıma sisteminin uygulanmasına odaklanır ve yüz sınıflandırıcı olarak Öklid Mesafesi (ED). PCA ve ICA'nın performansları üzerine bir karşılaştırma yapmıştır. Bu iki yöntem esas olarak bu araştırmada görüntü projeksiyonu ve boyutsallık azaltma için kullanılmaktadır. Sınıflandırma işlemi, ED sınıflandırıcısı tarafından benimsenen mesafe ölçü düzeni kullanılarak gerçekleştirilir. Karşılaştırma, belirli bir yüz görüntüsü kümesini tanıma açısından sistem sağlamlığı değerlendirmesi için alınır.

Kaynakça

  • [1] B. Rios-Sanchez, M. Viana-Matesanz, and C. Sanchez-Avila, “A comparative study of palmprint feature extraction methods for contact-less biometrics under different environmental conditions,” 2017 International Carnahan Conference on Security Technology (ICCST), Oct. 2017.
  • [2] G. Ragul, C. MageshKumar, R. Thiyagarajan, and R. Mohan, “Comparative study of statistical models and classifiers in face recognition,” 2013 International Conference on Information Communication and Embedded Systems (ICICES), Feb. 2013.
  • [3] M. M. Sani, K. A. Ishak, and S. A. Samad, “Evaluation of face recognition system using Support Vector Machine,” 2009 IEEE Student Conference on Research and Development (SCOReD), 2009.
  • [4] X. Li and G. Chen, “Face Recognition Based on PCA and SVM,” 2012 Symposium on Photonics and Optoelectronics, May. 2012.
  • [5] Y. Wu, Q. Nian, and S. Gu, “An improved Learning Evaluation system based on SVM for E-learning,” 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), Oct. 2012.
  • [6] T. Kaleekal and J. Singh, “Facial Expression recognition using higher order moments on facial patches,” 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Jul. 2019.
  • [7] W. Zhao, R. Chellappa, P.J. Philips, A. Rosenfeld, “Face recognition: A literature survey,” 2003Acm Computing Surveys (CSUR), Dec. 2003.
  • [8] F. Ahmad, A. Najam, and Z. Ahmed, “Image-based Face Detection and Recognition :"State of the Art ",’” Int. J. Comput. Sci. Issues, pp. 3–6, 2012.
  • [9] Chenggang Zhen and Yingmei Su, “Research about human face recognition technology,” 2009 International Conference on Test and Measurement, Dec. 2009.
  • [10] M. Nie, Y. Li, J. Zhang, and S. Wang, “The facial features analysis method based on human star-structured model,” 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE), Sep. 2019.
  • [11] E. Naz, U. Farooq, and T. Naz, “Analysis of Principal Component Analysis-Based and Fisher Discriminant Analysis-Based Face Recognition Algorithms,” 2006 International Conference on Emerging Technologies, 2006.
  • [12] M. A. Lone, S. M. Zakariya, and R. Ali, “Automatic Face Recognition System by Combining Four Individual Algorithms,” 2011 International Conference on Computational Intelligence and Communication Networks, Oct. 2011.
  • [13] H. Chen and J. Lu, “The image process technologies in face recognition,” The 2nd International Conference on Information Science and Engineering, Dec. 2010.
  • [14] S. Kar, S. Hiremath, D. G. Joshi, V. K. Chadda, and A. Bajpai, “A Multi-Algorithmic Face Recognition System,” 2006 International Conference on Advanced Computing and Communications, Dec. 2006. [15] I. M. Al-Bahri, S. O. Fageeri, A. M. Said, and G. M. A. Sagayee, “A Comparative Study Between PCA and Sift Algorithm for Static Face Recognition,” 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Feb. 2021.
  • [16] S. K. Dandpat and S. Meher, “Performance improvement for face recognition using PCA and two-dimensional PCA,” 2013 International Conference on Computer Communication and Informatics, Jan. 2013.
  • [17] L. Zunxiong, Z. Lihui, and Z. Heng, “Appearance-Based Subspace Projection Techniques for Face Recognition,” 2009 International Asia Symposium on Intelligent Interaction and Affective Computing, Dec. 2009.
  • [18] M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
  • [19] R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and machine recognition of faces: a survey,” Proceedings of the IEEE, vol. 83, no. 5, pp. 705–741, May 1995.
  • [20] S. Gushanskiy and V. Potapov, “Investigation of Quantum Algorithms for Face Detection and Recognition Using a Quantum Neural Network,” 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), May 2021.
  • [21] W. Chen, T. Sun, X. Yang, and L. Wang, “Face detection based on half face-template,” 2009 9th International Conference on Electronic Measurement & Instruments, Aug. 2009.
  • [22] S. K. Pakazad, F. Hajati, and S. D. Farahani, “A Face Detection Framework based on Selected Face Components,” Journal of Applied Sciences, vol. 11, no. 2, pp. 247–256, Jan. 2011.
  • [23] M. A. A. Akash, M. A. H. Akhand, and N. Siddique, “Robust Face Detection Using Hybrid Skin Color Matching under Different Illuminations,” 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Feb. 2019.
  • [24] A. Majumder, L. Behera, and V. K. Subramanian, “Automatic and Robust Detection of Facial Features in Frontal Face Images,” 2011 UkSim 13th International Conference on Computer Modelling and Simulation, Mar. 2011.
  • [25] A. A. Khair, Z. Zainuddin, A. Achmad, and A. A. Ilham, “Face Recognition in Kindergarten Students using the Principal Component Analysis Algorithm,” 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), Oct. 2019.
  • [26] Weilin Huang and H. Yin, “Linear and nonlinear dimensionality reduction for face recognition,” 2009 16th IEEE International Conference on Image Processing (ICIP), Nov. 2009.
  • [27] Shi-Fei Ding, Zhong-Zhi Shi, Yong Liang, and Feng-Xiang Jin, “Information feature analysis and improved algorithm of PCA,” 2005 International Conference on Machine Learning and Cybernetics, 2005.
  • [28] S. Z. Li and Juwei Lu, “Generalizing capacity of face database for face recognition,” Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998.
  • [29] L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” Journal of the Optical Society of America A, vol. 4, no. 3, p. 519, Mar. 1987. [30] A. Anand, M. A. Haque, J. S. R. Alex, and N. Venkatesan, “Evaluation of Machine learning and Deep learning algorithms combined with dimensionality reduction techniques for classification of Parkinson’s Disease,” 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Dec. 2018.
  • [31] Neerja and E. Walia, “Face Recognition Using Improved Fast PCA Algorithm,” 2008 Congress on Image and Signal Processing, 2008.
  • [32] S. Nedevschi, I. R. Peter, and A. Mandrut, “PCA type algorithm applied in face recognition,” 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, Aug. 2012.
  • [33] Lin Luo, M. N. S. Swamy, and E. I. Plotkin, “A modified PCA algorithm for face recognition,” CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).
  • [34] C. Tosik, A. Eleyan, and M. S. Salman, “Illumination invariant face recognition system,” 2013 21st Signal Processing and Communications Applications Conference (SIU), Apr. 2013.
  • [35] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Transactions on Neural Networks, vol. 13, no. 6, pp. 1450–1464, Nov. 2002.
  • [36] A. J. Goldstein, L. D. Harmon, and A. B. Lesk, “Identification of human faces,” Proceedings of the IEEE, vol. 59, no. 5, pp. 748–760, 1971.
  • [37] H. Deng, Z. Feng, Y. Liu, D. Luo, X. Yang, and H. Li, “Face Recognition Algorithm Based on Weighted Intensity PCNN,” 2020 Eighth International Conference on Advanced Cloud and Big Data (CBD), Dec. 2020.
  • [38] I. Buciu, C. kotropoulos, and I. Pitas, “ICA and Gabor representation for facial expression recognition,” Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
  • [39] M. Srinivasan and V. Aravamudhan, “Independent Component Analysis of Edge Information for Face Recognition under Variation of Pose and Illumination,” 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation, Sep. 2012.
  • [40] Y. Zhou, S. Cao, D. Wen, H. Zhang, and L. Zhao, “The study of face recognition based on hybrid principal components analysis and independent component analysis,” 2011 International Conference on Electronics, Communications and Control (ICECC), Sep. 2011.
  • [41] Y. Wang, H. Li, and Y. Guo, “Face recognition based on ICA and SPSO-ELM,” 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Dec. 2017.
  • [42] JingHua Cao, YanZhong Ran, and ZhiJun Xu, “A new Kernel function based face recognition algorithm,” 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, Aug. 2010.
  • [43] Jian Yang, D. Zhang, and Jing-Yu Yang, “Constructing PCA Baseline Algorithms to Re-evaluate ICA-Based Face-Recognition Performance,” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 37, no. 4, pp. 1015–1021, Aug. 2007.
  • [44] J. Yang, X. Gao, D. Zhang, and J. Yang, “Kernel ICA: An alternative formulation and its application to face recognition,” Pattern Recognition, vol. 38, no. 10, pp. 1784–1787, Oct. 2005.
  • [45] N. T. N. Babu, A. A. Fathima, and V. Vaidehi, “An Efficient Face Recognition System Using DWT-ICA Features,” 2011 International Conference on Digital Image Computing: Techniques and Applications, Dec. 2011.
  • [46] M. Luo, L. Song, and S. Li, “An Improved Face Recognition Based on ICA and WT,” 2012 IEEE Asia-Pacific Services Computing Conference, Dec. 2012.
  • [47] Y. Huang, M. Li, C. Lin, and L. Tian, “Gabor-Based Kernel Independent Component Analysis for Face Recognition,” 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Oct. 2010.
  • [48] B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with PCA and ICA,” Computer Vision and Image Understanding, vol. 91, no. 1–2, pp. 115–137, Jul. 2003.
  • [49] R. Thiyagarajan, S. Arulselvi, and G. Sainarayanan, “Gabor feature based classification using statistical models for face recognition,” Procedia Computer Science, vol. 2, pp. 83–93, 2010.
  • [50] Liwei Wang, Yan Zhang, and Jufu Feng, “On the Euclidean distance of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1334–1339, Aug. 2005.
  • [51] G. Thilagavathi and M. Suriakala, “Survey on face recognition,” 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), Apr. 2021.
  • [52] C. Chen and J. Tang, “Simulation study on the performance of several classifiers in face recognition,” 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, May 2012.
  • [53] D. Jelsovka, R. Hudec, and M. Breznan, “Face recognition on FERET face database using LDA and CCA methods,” 2011 34th International Conference on Telecommunications and Signal Processing (TSP), Aug. 2011.
  • [54] M. Grgic, K. Delac,2013. Face Recognition Homepage.[Online]. [Accessed 17 May 2021]. Available from: http://www.face-rec.org/databases/
  • [55] W. A. Barrett, “A survey of face recognition algorithms and testing results,” Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).
  • [56] Z. Lihong, W. Ye, and T. Hongfeng, “Face recognition based on independent component analysis,” 2011 Chinese Control and Decision Conference (CCDC), May 2011.
  • [57] Aalto University, Finlad, Department of Information and Computer Science, 2013. Independent Component Analysis (ICA) and Blind Source Separation (BSS). [Online].[Accessed 20 November 2013]. Available from: http://research.ics.aalto.fi/ica/fastica/
  • [58] S. Bag and G. Sanyal, “An efficient face recognition approach using PCA and minimum distance classifier,” 2011 International Conference on Image Information Processing, Nov. 2011.
  • [59] M. Imran, S. Noushath, A. Abdesselam, K. Jetly, and K. Karthikeyan, “Efficient multi-algorithmic approaches for face recognition using subspace methods,” 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), Feb. 2013.
  • [60] J.-H. Chen, C.-L. Lai, C.-H. Chu, and J.-Y. Hsu, “Improving accuracy of facial recognition systems for distant targets,” 2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE), Oct. 2013.
Toplam 58 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

Israa Alsaadı 0000-0003-0566-3308

Yayımlanma Tarihi 31 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 13 Sayı: 3

Kaynak Göster

IEEE I. Alsaadı, “Performance comparison of PCA and ICA algorithms-based face recognition system”, UTBD, c. 13, sy. 3, ss. 103–120, 2021.

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