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Towards large-scale face-based race classification on spark framework

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Abstract

Recently, the identification of an individual race has become an important research topic in face recognition systems, especially in large-scale face images. In this paper, we propose a new large-scale race classification method which combines Local Binary Pattern (LBP) and Logistic Regression (LR) on Spark framework. LBP is used to extract features from facial images, while spark’s logistic regression is used as a classifier to improve the accuracy and speedup the classification system. The race recognition method is performed on Spark framework to process, in a parallel way, a large scale of data. The evaluation of our proposed method has been performed on two large face image datasets CAS-PEAL and Color FERET. Two major races were considered for this work, including Asian and Non-Asian races. As a result, we achieve the highest race classification accuracy (99.99%) compared to Linear SVM, Naive Bayesian (NB), Random Forest(RF), and Decision Tree (DT) Spark’s classifiers. Our method is compared against different state-of-the-art methods on race classification, the obtained results show that our approach is more efficient in terms of accuracy and processing time.

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Notes

  1. Race Wiki (Race human classification): http://en.wikipedia.org/wiki/

  2. https://spark.apache.org/docs/1.6.1/mllib-classification-regression.html

  3. CAS-PEAL Face Database: http://www.jdl.ac.cn/peal/index.html

  4. Color FERET Database Share: https://www.nist.gov/itl/iad/image-group/color-feret-database

  5. Linear Methods RDD-based API: https://spark.apache.org/docs/2.2.0/mllib-linear-methods.html/logistic-regression

  6. CAS-PEAL Face Database: http://www.jdl.ac.cn/peal/index.html

  7. color FERET Database Share: https://www.nist.gov/itl/iad/image-group/color-feret-database

References

  1. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell, 28(12):2037–2041

  2. Anwar I, Islam NU (2017) Learned features are better for ethnicity classification. Cybernetics and Information Technologies 17(3):152–164

    Article  Google Scholar 

  3. Chen C, Ross A (2013) Local gradient gabor pattern (lggp) with applications in face recognition, cross-spectral matching, and soft biometrics. In: Biometric and surveillance technology for human and activity identification X. International Society for Optics and Photonics, vol 8712, p 87120R

  4. Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113

    Article  Google Scholar 

  5. Eom S, Huh J-H (2018) Group signature with restrictive linkability: minimizing privacy exposure in ubiquitous environment. J Ambient Intell Humaniz Comput, pp 1–11

  6. Fu S, He H, Hou Z-G (2014) Learning race from face: a survey. IEEE Trans Pattern Anal Mach Intell 36(12):2483–2509

    Article  Google Scholar 

  7. Gao W, Cao B, Shan S, Chen X, Zhou D, Zhang X, Zhao D (2008) The cas-peal large-scale chinese face database and baseline evaluations. IEEE Trans Syst Man Cybern Part A Syst Hum 38(1):149–161

    Article  Google Scholar 

  8. Gutta S, Huang J, Jonathon P, Wechsler H (2000) Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Trans Neural Netw 11(4):948–960

    Article  Google Scholar 

  9. Gutta S, Wechsler H (1999) Gender and ethnic classification of human faces using hybrid classifiers. In: IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339), vol 6. IEEE, pp 4084–4089

  10. Gutta S, Wechsler H, Phillips PJ (1998) Gender and ethnic classification of face images. In: Proceedings third IEEE international conference on automatic face and gesture recognition. IEEE, pp 194–199

  11. Han H, Otto C, Liu X, Jain AK (2015) Demographic estimation from face images: human vs. machine performance. IEEE Trans Pattern Anal Mach Intell 37(6):1148–1161

    Article  Google Scholar 

  12. Harnie D, Saey M, Vapirev AE, Wegner JK, Gedich A, Steijaert M, Ceulemans H, Wuyts R, De Meuter W (2017) Scaling machine learning for target prediction in drug discovery using apache spark. Futur Gener Comput Syst 67:409–417

    Article  Google Scholar 

  13. Huh J-H (2018) Plc-integrated sensing technology in mountain regions for drone landing sites: focusing on software technology. Sensors 18(8):2693

    Article  Google Scholar 

  14. Ibrahim D, Hamdy S (2017) Parallel architecture for face recognition using mpi. (IJACSA) Int J Adv Comput Sci Appl 8(1):425–430

    MathSciNet  Google Scholar 

  15. Karau H, Konwinski A, Wendell P, Zaharia M (2015) Learning spark: lightning-fast big data analysis, 1st edn. O’Reilly Media, Inc. ISBN 1449358624, 9781449358624

  16. Lahdenoja O, Maunu J, Laiho M, Paasio A (2006) A massively parallel algorithm for local binary pattern based face recognition. In: 2006 IEEE international symposium on circuits and systems. IEEE, p 4

  17. Lee S, Huh J-H (2018) An effective security measures for nuclear power plant using big data analysis approach. J Supercomput, pp 1–28. https://doi.org/10.1007/s11227-018-2440-4

  18. Li S, Li C, Chen G, Bourbakis NG, Lo K-T (2008) A general quantitative cryptanalysis of permutation-only multimedia ciphers against plaintext attacks. Signal Process Image Commun 23(3):212–223

    Article  Google Scholar 

  19. Lu X, Jain AK et al (2004) Ethnicity identification from face images. In: Proceedings of SPIE, vol 5404, pp 114–123

  20. Manesh FS, Ghahramani M, Tan Y-P (2010) Facial part displacement effect on template-based gender and ethnicity classification. In: 2010 11th international conference on control automation robotics & vision. IEEE, pp 1644–1649

  21. Masood S, Gupta S, Wajid A, Gupta S, Ahmed M (2018) Prediction of human ethnicity from facial images using neural networks. In: Data engineering and intelligent computing. Springer, pp 217–226

  22. Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S et al (2016) Mllib: machine learning in apache spark. J Mach Learn Res 17(1):1235–1241

    MathSciNet  MATH  Google Scholar 

  23. Momin H, Tapamo J-R (2016) A comparative study of a face components based model of ethnic classification using gabor filters. Applied Mathematics & Information Sciences 10(6):2255–2265

    Article  Google Scholar 

  24. Muhammad G, Hussain M, Alenezy F, Bebis G, Mirza AM, Aboalsamh H (2012a) Race classification from face images using local descriptors. Int J Artif Intell Tools 21(05):1250019

    Article  Google Scholar 

  25. Muhammad G, Hussain M, Alenezy F, Bebis G, Mirza AM, Aboalsamh H (2012b) Race classification from face images using local descriptors. Int J Artif Intell Tools 21(05):1250019

    Article  Google Scholar 

  26. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  27. Ou Y, Wu X, Qian H, Xu Y (2005) A real time race classification system. In: 2005 IEEE international conference on information acquisition. IEEE, p 6

  28. Panaousis EA, Nazaryan L, Politis C (2009) Securing AODV against wormhole attacks in emergency MANET multimedia communications. In: Proceedings of the 5th international ICST mobile multimedia communications conference, London, pp 34:1–34:7

  29. Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The feret database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16(5):295–306

    Article  Google Scholar 

  30. Qateef JS, Kazm AA (2016) Facial expression recognition via mapreduce assisted k-nearest neighbor algorithm. Int J Comput Sci Inform Secur 14(2):170

    Google Scholar 

  31. Roomi SMM, Virasundarii S, Selvamegala S, Jeevanandham S, Hariharasudhan D (2011) Race classification based on facial features. In: 2011 third national conference on computer vision, pattern recognition, image processing and graphics. IEEE, pp 54–57

  32. Salah SH, Du H, Al-Jawad N (2013) Fusing local binary patterns with wavelet features for ethnicity identification. In: Proceedings of world academy of science, engineering and technology. World Academy of Science, Engineering and Technology (WASET), p 471

  33. Shafer J, Rixner S, Cox AL (2010) The hadoop distributed filesystem: Balancing portability and performance. In: 2010 IEEE international symposium on performance analysis of systems & software (ISPASS). IEEE, pp 122–133

  34. Verschae R, Ruiz-del Solar J, Correa M (2006) Gender classification of faces using adaboost. In: Iberoamerican congress on pattern recognition. Springer, pp 68–78

  35. Wu L, Du X, Fu X (2014) Security threats to mobile multimedia applications: camera-based attacks on mobile phones. IEEE Commun Mag 52(3):80–87

    Article  Google Scholar 

  36. Xie Y, Luu K, Savvides M (2012) A robust approach to facial ethnicity classification on large scale face databases. In: 2012 IEEE fifth international conference on biometrics: theory, applications and systems (BTAS). IEEE, pp 143–149

  37. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin MJ, Shenker S, Stoica I (2012) Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX conference on networked systems design and implementation. USENIX Association, pp 2–2

  38. Zhao HV, Wu M, Wang ZJ, Liu KR (2005) Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting. IEEE Trans Image Process 14(5):646–661

    Article  Google Scholar 

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Correspondence to Mezzoudj Saliha.

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Saliha, M., Ali, B. & Rachid, S. Towards large-scale face-based race classification on spark framework. Multimed Tools Appl 78, 26729–26746 (2019). https://doi.org/10.1007/s11042-019-7672-7

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