EVALUATION OF ACCURACY PERFORMANCE IN FACE RECOGNITION SYSTEMS

Year 2019, , 835 - 842, 19.12.2019

Bekir Aksoy

https://doi.org/10.21923/jesd.559887

Cited By: 1

Abstract

The importance of face
recognition systems has increased in recent years. In this study, front, side,
upper and lower facial images of 41 people consisting of volunteer students and
faculty members from Isparta University of Applied Sciences were taken and LDA,
LBP-PCA and SVD facial recognition algorithms were applied and their model was
obtained. The obtained models were classified on the test face images and
evaluated according to the RMSE and MAPE performance criteria. In the front
face and side face recognition system, PCA and SVD algorithm, in the upper and
lower face recognition system LBP algorithm were found to give the best
results.

Keywords

Image Processing, Face Recognition, LDA Algorithm, PCA and SVD Algorithm, LBP Algorithm

YÜZ TANIMA SİSTEMLERİNDE DOĞRULUK PERFORMANSLARININ DEĞERLENDİRİLMESİ

Abstract

Yüz tanıma
sistemlerinin güvenlik açısından önemi son yıllarda oldukça artmıştır.
Çalışmada, Isparta Uygulamalı Bilimler Üniversitesi Teknoloji Fakültesindeki
gönüllü öğrenci ve öğretim üyelerinden oluşan 41 kişiye ait ön, yan, üst ve alt
yüz görüntüleri alınarak LDA, LBP ve PCA ile SVD yüz tanıma algoritmaları
uygulanarak her birine ait model elde edilmiştir.  Elde edilen modeller test yüz görüntüleri
üzerinde sınıflandırılarak, RMSE ve MAPE performans ölçüt kriterlerine göre
değerlendirilerek ön ve yan yüz tanıma sisteminde PCA ve SVD algoritması, üst ve alt yüz tanıma sisteminde ise LBP
Algoritmasının en iyi sonucu verdiği tespit edilmiştir.

Keywords

Görüntü İşleme, Yüz Tanıma, LDA Algoritması, PCA ve SVD Algoritması, LBP Algoritması

References

• Abudarham, N., Shkiller, L., & Yovel, G., 2019. Critical features for face recognition. Cognition, 182, 73-83. https://doi.org/10.1016/j.cognition.2018.09.002.
• Aburomman, A. A., & Reaz, M. B. I., 2016. Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection. In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 636-640, IEEE. DOI: 10.1109/IMCEC.2016.7867287
• Banerjee, S., & Das, S., 2018. LR-GAN for degraded Face Recognition. Pattern Recognition Letters, 116, 246-253. https://doi.org/10.1016/j.patrec. 2018. 10.034
• Barbedo, J. G. A., 2018. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computers and Electronics in Agriculture, 153, 46–53. https://doi.org/10.1016/ j.compag. 2018. 08.013
• Becerra-Riera, F., Morales-González, A., & Méndez-Vázquez, H., 2018. Facial marks for improving face recognition. Pattern Recognition Letters, 113, 3-9. https://doi.org/10.1016/j.patrec. 2017. 05. 005.
• Bengio, Y., 2009. Learning deep architectures for AI. Foundations and trends in Machine Learning, 2(1), 1-127.
• Borade, S. N., Deshmukh, R. R., & Ramu, S., 2016. Face recognition using fusion of PCA and LDA: Borda count approach. In 2016 24th Mediterranean conference on control and automation (MED) 1164-1167, IEEE.
• Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250.
• Ding, C., & Tao, D., 2017. Pose-invariant face recognition with homography-based normalization. Pattern Recognition, 66, 144-152. https://doi.org/10.1016/j.patcog.2016.11.024
• Fronckova, K., Prazak, P., & Slaby, A., 2018. Singular Value Decomposition and Principal Component Analysis in Face Images Recognition and FSVDR of Faces. In International Conference on Information Systems Architecture and Technology, 105-114. Springer, Cham.
• Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., ... & Whyntie, T., 2018. NiftyNet: a deep-learning platform for medical imaging. Computer methods and programs in biomedicine, 158, 113-122.
• Greenspan, H., Van Ginneken, B., & Summers, R. M., 2016. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, 35(5), 1153-1159. https://doi.org/10.1109/TMI.2016.2553401.
• Goljan, M., Chen, M., Comesaña, P., & Fridrich, J., 2016. Effect of compression on sensor-fingerprint based camera identification. Electronic Imaging, 2016(8), 1-10. https://doi.org/10.2352/ISSN.2470-1173. 2016.8.MWSF-086.
• Gonzalez, M. A., Baraloto, C., Engel, J., Mori, S. A., Pétronelli, P., Riéra, B., ... & Chave, J., 2009. Identification of Amazonian trees with DNA barcodes. PLoS one, 4(10), e7483. https://doi.org/10.1371/journal.pone.0007483
• Guo, D., Zhong, M., Ji, H., Liu, Y., & Yang, R., 2018. A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors. Neurocomputing, 319, 155-163.
• Hamdan, B., & Mokhtar, K., 2018. Face recognition using angular radial transform. Journal of King Saud University-Computer and Information Sciences, 30(2), 141-151. https://doi.org/ 10. 1016/ j.jksuci.2016.10.006.
• https://medium.freecodecamp.org/want-to-know-how-deep-learning-works-heres-a-quick-guide-for-everyone-1aedeca88076 Accessed April 24, 2019.
• Hu, J., 2017. Discriminative transfer learning with sparsity regularization for single-sample face recognition. Image and vision computing, 60, 48-57.
• Jain, U., Choudhary, K., Gupta, S., & Privadarsini, M. J. P., 2018. Analysis of Face Detection and Recognition Algorithms Using Viola Jones Algorithm with PCA and LDA. In 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 945-950. IEEE. doi: 10.1109/ICOEI.2018.8553811
• Kamencay, P., Trnovszky, T., Benco, M., Hudec, R., Sykora, P., & Satnik, A., 2016. Accurate wild animal recognition using PCA, LDA and LBPH, 62-67, IEEE. DOI: 10.1109/ELEKTRO.2016.7512036
• Kim, S., & Kim, H., 2016. A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669-679. https://doi.org/10.1016/ j.ijforecast.2015.12.003.
• Lahdenoja, O., Laiho, M., & Paasio, A., 2005. Reducing the feature vector length in local binary pattern based face recognition. In IEEE International Conference on Image Processing 2005, 2, II-914. IEEE.
• Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. 2017. A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.005.
• Liu, Y., Wang, H., Su, Z., & Fan, J., 2018. Deep learning based trajectory optimization for UAV aerial refueling docking under bow wave. Aerospace Science and Technology, 80, 392-402.
• Lu, Z., Jiang, X., & Kot, A., 2018. Color Space Construction by Optimizing Luminance and Chrominance Components for Face Recognition. Pattern Recognition. https://doi.org/10.1016/ j. patcog.2018.06.015
• Lundervold, A. S., & Lundervold, A., 2018. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik, 29,102-127. https://doi.org/10.1016/ j.zemedi. 2018. 11. 002.
• Madu, C. N., Kuei, C. H., & Lee, P., 2017. Urban sustainability management: A deep learning perspective. Sustainable cities and society, 30, 1-17.
• Mairal, J., Bach, F., & Ponce, J., 2014. Sparse modeling for image and vision processing. Foundations and Trends® in Computer Graphics and Vision, 8(2-3), 85-283. http://dx.doi.org/10.1561/0600000058.
• Micheva, I., Ziros, P., Pherson, L., Giannakoulas, N., Symeonidis, A., & Zoumbos, N., 2005. Involvement of ERK, p38 and NFκB Signalling in the Maturation Defects of Monocyte Derived Dendritic Cells in Patients with Myelodysplastic Syndrome. https://doi.org/10.5272/jimab.2014206.542
• Oh, S. K., Yoo, S. H., & Pedrycz, W., 2013. Design of face recognition algorithm using PCA-LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks: Design and its application. Expert Systems with Applications, 40(5), 1451-1466. https://doi.org/10.1016/ j.eswa.2012.08.046.
• Parkhi, O. M., Vedaldi, A., & Zisserman, A., 2015. Deep face recognition. In BMVC 1(3),6.
• Schmidhuber, J., 2015. Deep learning in neural networks: An overview. Neural networks, 61, 85-117. https://doi.org/10.1016/j.neunet. 2014.09. 003
• Shi, X., Yang, Y., Guo, Z., & Lai, Z., 2014. Face recognition by sparse discriminant analysis via joint L2, 1-norm minimization. Pattern Recognition, 47(7), 2447-2453. https://doi.org/10.1016/j.patcog. 2014.01.007
• Shih, P., & Liu, C., 2005. Comparative assessment of content-based face image retrieval in different color spaces. International Journal of Pattern Recognition and Artificial Intelligence, 19(07), 873-893. https://doi.org/10.1142/ S0218001405004381
• Singh, A. K., Ganapathysubramanian, B., Sarkar, S., & Singh, A., 2018. Deep learning for plant stress phenotyping: trends and future perspectives. Trends in plant science, 23, 883-898. https://doi.org/10.1016/j.tplants.2018.07.004.
• Smith, D. F., Wiliem, A., & Lovell, B. C., 2015. Face recognition on consumer devices: Reflections on replay attacks. IEEE Transactions on Information Forensics and Security, 10(4), 736-745.
• Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A., 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI 4, 4278-4284.
• Tiwari, P., Qian, J., Li, Q., Wang, B., Gupta, D., Khanna, A., ... & de Albuquerque, V. H. C., 2018. Detection of subtype blood cells using deep learning. Cognitive Systems Research, 52, 1036-1044.
• Vinay, A., Vasuki, V., Bhat, S., Jayanth, K. S., Murthy, K. B., & Natarajan, S., 2016. Two dimensionality reduction techniques for surf based face recognition. Procedia Computer Science, 85, 241-248.
• Yang, A., Wang, Q., & Cao, J., 2019. Research on Adaptive Face Recognition Algorithm Under Low Illumination Condition. In Advances in Graphic Communication, Printing and Packaging(pp. 266-272). Springer, Singapore. https://doi.org /10.1007/978-981 -13-3663-8_37
• Yang, B., & Chen, S., 2013. A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing, 120, 365-379.
• You, J., Li, W., & Zhang, D., 2002. Hierarchical palmprint identification via multiple feature extraction. Pattern recognition, 35(4), 847-859. https://doi.org/10.1016/S0031-3203(01)00100-5
• Zainuddin, Z., & Laswi, A. S., 2017. Implementation of the LDA algorithm for online validation Based on face recognition. In Journal of Physics: Conference Series, 801 (1), 12-47). IOP Publishing. doi:10.1088/1742-6596/801/1/012047.
• Zhang, B., Gao, Y., Zhao, S., & Liu, J., 2019. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE transactions on image processing, 19(2), 533-544.
• Zhang, B., Gao, Y., Zhao, S., & Liu, J., 2010. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE transactions on image processing, 19(2), 533-544.
• Zhao, W., Krishnaswamy, A., Chellappa, R., Swets, D. L., & Weng, J., 1998. Discriminant analysis of principal components for face recognition. In Face Recognition,73-85, Springer, Berlin, Heidelberg.
• Zhi, H., & Liu, S., 2019. Face recognition based on genetic algorithm. Journal of Visual Communication and Image Representation, 58, 495-502. https://doi.org/10.1016/ j.jvcir. 2018. 12.012.
• Zhong, L., Hu, L., & Zhou, H., 2019. Deep learning based multi-temporal crop classification. Remote Sensing of Environment, 221, 430-443.