nach oben

Arabian Journal for Science and Engineering

Erschienen in:

11.05.2021 | Research Article-Computer Engineering and Computer Science

The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset

verfasst von: Muhammed Ali Erbir, Halil Murat Ünver

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 9/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems.

Vorheriger Artikel A Truly Dynamic Substitution Box Generator for Block Ciphers Based on Elliptic Curves Over Finite Rings

Nächster Artikel Arabic Sentiment Analysis Using Deep Learning and Ensemble Methods

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Zeiler, M.D.; Fergus, R.: Visualizing and understanding convolutional networks (2014). https://doi.org/10.1007/978-3-319-10590-1_53

Erhan, D.; Bengio, Y.; Courville, A.; Vincent, P.: Visualizing higher-layer features of a deep network. Bernoulli 1341, 1 (2009)

I. L. S. V. R. Competition. LSVRC (2012). http://www.image-net.org/challenges. Accessed 27 Dec 2016

Krizhevsky, A.; Sutskever, I.; Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2, 1097–1105 (2012)

He, K.; Zhang, X.; Ren, S.; Sun, J.: Deep residual learning for image recognition (2016). https://doi.org/10.1109/CVPR.2016.90

Dollár, P.; Wojek, C.; Schiele, B.; Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. (2012). https://doi.org/10.1109/TPAMI.2011.155CrossRef

Yang, M.H.; Kriegman, D.J.; Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2002). https://doi.org/10.1109/34.982883CrossRef

Ye, Q.; Doermann, D.: Text detection and recognition in imagery: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2015). https://doi.org/10.1109/TPAMI.2014.2366765CrossRef

Sun, Z.; Bebis, G.; Miller, R.: On-road vehicle detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. (2006). https://doi.org/10.1109/TPAMI.2006.104CrossRef

10.

Pinkney, J.: MTCNN Face Detection. MatWorks (2020). https://www.github.com/matlab-deep-learning/mtcnn-face-detection

11.

Graves, A.; Jaitly, N.; Mohamed, A.R.: Hybrid speech recognition with deep bidirectional LSTM (2013).: https://doi.org/10.1109/ASRU.2013.6707742

12.

Spanhol, F.A.; Oliveira, L.S.; Petitjean, C.; Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. (2016). https://doi.org/10.1109/TBME.2015.2496264CrossRef

13.

Al-Ayyoub, M.; Alzu’Bi, S.M.; Jararweh, Y.; Alsmirat, M.A.: A GPU-based breast cancer detection system using single pass fuzzy C-means clustering algorithm. In: International Conference on Multimedia Computing and Systems—Proceedings, pp. 650–654 (2017). https://doi.org/10.1109/ICMCS.2016.7905595

14.

Alzu’Bi, S.; Al-Qatawneh, S.; Alsmirat, M.: Transferable HMM trained matrices for accelerating statistical segmentation time. In: 2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018, pp. 172–176 (2018). https://doi.org/10.1109/SNAMS.2018.8554487

15.

Sutskever, I.; Hinton, G.; Krizhevsky, A.; Salakhutdinov, R.R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)MathSciNetMATH

16.

Huang, G.B.; Mattar, M.; Berg, T.; Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments (2008)

17.

Cao, Z.; Yin, Q.; Tang, X.; Sun, J.: Face recognition with learning-based descriptor (2010). https://doi.org/10.1109/CVPR.2010.5539992

18.

Chen, D.; Cao, X.; Wen, F.; Sun, J.: Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification (2013). https://doi.org/10.1109/CVPR.2013.389

19.

Simonyan, K.; Parkhi, O.M.; Vedaldi, A.; Zisserman, A.: Fisher vector faces in the wild (2013). https://doi.org/10.5244/C.27.8

20.

Sun, Y.; Wang, X.; Tang, X.: Deep learning face representation from predicting 10,000 classes (2014). https://doi.org/10.1109/CVPR.2014.244

21.

Bansal, A.; Castillo, C.; Ranjan, R.; Chellappa, R.: The do’s and don’ts for CNN-Based face verification (2017). https://doi.org/10.1109/ICCVW.2017.299

22.

Phillips, P.J.; Wechsler, H.; Huang, J.; Rauss, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. (1998). https://doi.org/10.1016/s0262-8856(97)00070-xCrossRef

23.

Georghiades, A.S.; Belhumeur, P.N.; Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. (2001). https://doi.org/10.1109/34.927464CrossRef

24.

Georghiades, A.: Yale Face Database. Cent. Comput. Vis. Control Yale Univ. (1997). http//cvc.yale.edu/projects/yalefaces/yalefa

25.

Ricanek, K.; Tesafaye, T.: MORPH: a longitudinal image database of normal adult age-progression (2006).: https://doi.org/10.1109/FGR.2006.78

26.

Kemelmacher-Shlizerman, I.; Seitz, S.M.; Miller, D.; Brossard, E.: The MegaFace benchmark: 1 million faces for recognition at scale (2016). https://doi.org/10.1109/CVPR.2016.527

27.

Guo, Y.; Zhang, L.; Hu, Y.; He, X.; Gao, J.: MS-celeb-1M: a dataset and benchmark for large-scale face recognition (2016). https://doi.org/10.1007/978-3-319-46487-9_6

28.

Cao, Q.; Shen, L.; Xie, W.; Parkhi, O.M.; Zisserman, A.: VGGFace2: a dataset for recognising faces across pose and age (2018). https://doi.org/10.1109/FG.2018.00020

29.

Şafak, E.; Barışçı, N.: Age and gender prediction using convolutional neural networks. In: 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–7 (2018)

30.

Berg, T.; Belhumeur, P.N.: Tom-vs-Pete classifiers and identity-preserving alignment for face verification (2012). https://doi.org/10.5244/C.26.129

31.

Berg, T.; Belhumeur, P.N.: POOF: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation (2013). https://doi.org/10.1109/CVPR.2013.128

32.

Cao, X.; Wipf, D.; Wen, F.; Duan, G.; Sun, J.: A practical transfer learning algorithm for face verification (2013). https://doi.org/10.1109/ICCV.2013.398

33.

Chen, D.; Cao, X.; Wang, L.; Wen, F.; Sun, J.: Bayesian face revisited: a joint formulation (2012). https://doi.org/10.1007/978-3-642-33712-3_41

34.

Guillaumin, M.; Verbeek, J.; Schmid, C.: Is that you? Metric learning approaches for face identification (2009). https://doi.org/10.1109/ICCV.2009.5459197

35.

Huang, G.B.; Lee, H.; Learned-Miller, E.: Learning hierarchical representations for face verification with convolutional deep belief networks (2012).https://doi.org/10.1109/CVPR.2012.6247968

36.

Kumar, N.; Berg, A.C.; Belhumeur, P.N.; Nayar, S.K.: Attribute and simile classifiers for face verification (2009). https://doi.org/10.1109/ICCV.2009.5459250

37.

Sun, Y.; Liang, D.; Wang, X.; Tang, X.: Deepid3: face recognition with very deep neural networks (2015). arXiv Prepr. arXiv1502.00873

38.

Jayaraman, U.; Gupta, P.; Gupta, S.; Arora, G.; Tiwari, K.: Recent development in face recognition. Neurocomputing (2020). https://doi.org/10.1016/j.neucom.2019.08.110CrossRef

39.

Viola, P.; Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. (2004). https://doi.org/10.1023/B:VISI.0000013087.49260.fbCrossRef

40.

Masi, I.; Wu, Y.; Hassner, T.; Natarajan, P.: Deep face recognition: a survey (2019). https://doi.org/10.1109/SIBGRAPI.2018.00067

41.

Simonyan, K.; Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv Prepr. arXiv1409.1556

42.

Szegedy, C.; et al.: Going deeper with convolutions (2015). https://doi.org/10.1109/CVPR.2015.7298594

43.

Michele, A.; Colin, V.; Santika, D.D.: Mobilenet convolutional neural networks and support vector machines for palmprint recognition (2019). https://doi.org/10.1016/j.procs.2019.08.147

44.

Iandola, F.N.; Moskewicz, M.W.; Ashraf, K.; Han, S.; Dally, W.J.; Keutzer, K.: SqueezeNet. arXiv (2016)

45.

Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z.:Rethinking the Inception Architecture for Computer Vision (2016). doi: https://doi.org/10.1109/CVPR.2016.308.

46.

Domhan, T.; Springenberg, J.T.; Hutter, F.: Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves (2015)

47.

Pan, S.J.; Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. (2010). https://doi.org/10.1109/TKDE.2009.191CrossRef

48.

Hasan, M.K.; Pal, C.J.: Improving alignment of faces for recognition (2011). https://doi.org/10.1109/ROSE.2011.6058545

49.

Matlab, S.: What is MATLAB? MathWorks, Natick (2012)

50.

Deng, J.; Guo, J.; Xue, N.; Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition (2019). https://doi.org/10.1109/CVPR.2019.00482

Titel: The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset
verfasst von: Muhammed Ali Erbir
Halil Murat Ünver
Publikationsdatum: 11.05.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 9/2021
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-05693-6

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 9/2021

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

Software Package Restructuring with Improved Search-based Optimization and Objective Functions

A Jungle Community Detection Algorithm Based on New Weighted Similarity

Reversible Data Hiding for Electronic Patient Information Security for Telemedicine Applications

Optimal Selection of Features Using Artificial Electric Field Algorithm for Classification

A Comparative Assessment of Time Series Forecasting Using NARX and SARIMA to Predict Hourly, Daily, and Monthly Global Solar Radiation Based on Short-Term Dataset

Premium Partner

Marktübersichten