Abstract
This paper presents an exhaustive component-based analysis to identify the ethnicity from facial images. The different ethnic groups identified are Asian, African, African American, Asian Middle East, Caucasian and Other. The classification techniques investigated include Decision Trees, Naïve Bayes, Random Forest and K-Nearest Neighbor. Naïve Bayes achieved 84.7 % and 85.6 % accuracy rates for African ethnicity and Asian ethnicity identification, respectively. The Decision Trees achieved 85.8 % for African American ethnicity identification rate, while K-Nearest Neighbor achieved 86.8 % for Asian Middle East ethnicity and Random Forest achieved 90.8 % for Caucasian ethnicity identification rate. This research work achieved an overall ethnicity identification rate of 86.6 %.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lu, X.: Image analysis for face recognition. Personal notes, 5 May (2003)
Lu, X., Jain, A.K.: Ethnicity identification from face images. In: Defense and Security, International Society for Optics and Photonics, pp. 114–123 (2004)
Buchala, S., Davey, N., Gale, T.M., Frank, R.J.: Principal component analysis of gender, ethnicity, age, and identity of face images. In: Proceedings of IEEE ICMI (2005)
Tin, H.H.K., Sein, M.M.: Race identification for face images. ACEEE Int. J. Inform. Tech. 1(02) (2011)
Mulcahy, C.: Image compression using the haar wavelet transform. Spelman Sci. Math. J. 1(1), 22–31 (1997)
Teague, M.R.: Image analysis via the general theory of moments. JOSA 70(8), 920–930 (1980)
Berisha, S.: Image classification using gabor filters and machine learning (2009)
Salah, S.H., Du, H., Al-Jawad, N.: Fusing local binary patterns with wavelet features for ethnicity identification. In: Proceedings of IEEE International Conference on Signal Image Process, vol. 21, pp. 416–422 (2013)
Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)
Lowd, D., Domingos, P.: Naive bayes models for probability estimation. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 529–536. ACM (2005)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)
Milborrow, S., Morkel, J., Nicolls, F.: The MUCT landmarked face database. In: Pattern Recognition Association of South Africa (2010)
Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pp. 138–142. IEEE (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Boyseens, A., Viriri, S. (2016). Component-Based Ethnicity Identification from Facial Images. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-46418-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46417-6
Online ISBN: 978-3-319-46418-3
eBook Packages: Computer ScienceComputer Science (R0)