Race Classification from Face Images Using Fast Fourier Transform and Discrete Cosine Transform

Authors

  • Hawkar O. Ahmed Department of Information Technology, College of Commerce, University of Sulaimani, Kurdistan Region, Iraq. & Department of Information Technology, University College of Goizha, Sulaimani, Kurdistan Region, Iraq. Author

DOI:

https://doi.org/10.17656/jzs.10816

Keywords:

FFT, DCT, Fusion, Knn

Abstract

Ethnicity identification and recognition is a key biometric technology with a wide range of applications related to homeland security, safety, access control, and automatic annotation. Ethnicity identification from face images is a process of gathering facial features of an individual face image compared to existing face images in the dataset to interpretation his/her ethnic class. In this paper, a propose method in multi-level fusion schema for ethnicity identification by using two global features; fast Fourier transform (FFT) and discrete cosine transform (DCT) on the pre-processed face image of size 128 * 128 in YCbCr color space. A dataset is consisting of 750 face image of three different ethnicities (Kurd 300, Oriental 300 and African 150). The query image feature is compared with a dataset image features using k – nearest neighbor classifier using City block distance for evaluating similarity measurement. The experimental result shows good accuracy and demonstrate the effectiveness of the combined features reached an accuracy rate 96.22% of classification.

References

Ghulam M., Muhammad H. and Fatmah A. "Race Classification From Face Images Using Local Descriptors". International Journal on Artificial Intelligence Tools. Vol. 21, No. 5. (2012). DOI: https://doi.org/10.1142/S0218213012500194

Lu, X. and Jain, A. K. "Ethnicity identification from face images". Proc. SPIE International Symposium on Defense and Security: Biometric Technology for Human Identification, Orlando, Florida, April. pp. 114-123. (2004).

Manesh, F. S., Ghahramani, M., and Tan, Y. "Facial part displacement effect on template-based gender and ethnicity classification". Control Automation Robotics & Vision (ICARCV), Proc. 11th International Conference on Control, Automation, Robotics and Vision, Singapore, December, pp. 1644-1649. (2010). DOI: https://doi.org/10.1109/ICARCV.2010.5707882

Xiaoguang Lu, Anil K. Jain. "Ethnicity Identfication from Face Images [J]". Bull.Inst. Math. Acad. Sinica. Vol. 33, pp. 77-87. (2005).

Satoshi Hosoi, Erina Takikawa, Masato Kawade. "Ethnicity Estimation with Facial Image [C]". Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition. pp. 10-16. (2004).

Salah, S. H., Du, H. and Al-Jawad, N. "Fusing local binary patterns with wavelet features for ethnicity identification". Proc. ICSIP 2013: International Journal of Computer, Information Science and Engineering. Vol. 7, No. 7, pp. 330-336. (2013).

Faraidoon H. & Aree A. "Hybrid Wavelet and Discrete Cosine Transform Methods for Ethnicity Identification". ‎JZS-A. Vol. 17, No. 1. (2015). DOI: https://doi.org/10.17656/jzs.10366

Hongbo Du; Sheerko H. Salah; Hawkar O. Ahmed. "A color and texture based multi-level fusion scheme for ethnicity identification". Published in Proceedings Volume 9120: Mobile Multimedia/Image Processing, Security, and Applications (June 2014).

Ziad M. Hafed and Martin D. Levine. "Face recognition using the discrete cosine transform". Vol. 43, pp. 167–188. (2001). DOI: https://doi.org/10.1023/A:1011183429707

Ritu K. and Susmita M. "Fingerprint Based Gender Identification Using Frequency Domain Analysis". IJAET. March (2012).

Y. Rangaswamy; K. B. Raja; K.R. Venugopal. "FRDF: Face Recognition using of DTCWT and FFT Features". ScienceDirect, Procidia Computer Science. Vol. 54, pp. 809-817. (2015). DOI: https://doi.org/10.1016/j.procs.2015.06.095

P. Phillips, H. Moon, S. A.Rizvi, and P. J. Rauss. "The feret evaluation methodology for face-recognition algorithms". IEEE Trans. PAMI. Vol. 22, No. 10, pp. 1090–1104. DOI: https://doi.org/10.1109/34.879790

Burton, A., White, D., and McNeill, A. "The Glasgow face matching test. Behavior research methods". Vol. 42, No. 1, pp. 286-291. (2010). DOI: https://doi.org/10.3758/BRM.42.1.286

T. Sim, S. Baker, and M. Bsat. "The cmu pose, illumination, and expression (pie) dataset". Pp. 53–58. (2002).

Milborrow, S., Morkel, J. and Nicolls, F. "The MUCT landmarked face dataset". Pattern Recognition Association of South Africa. (2010).

Teknomo K. "What is K Nearest Neighbors Algorithm". htp://people.revoledu.com/kardi/tutorial/KNN/Contents.htm.

D'Amato C,Malerba D,Esposito F,et al. "Extending the K-Nearest Neighbour classification algorithm to symbolic objects[C] ". Convegno Scientifico Intermedio SIS. Universit -àdegli Studi di Napoli"FedericoĊ, 9-11 Giugno (2003).

S. Hosoi, E. Takikawa, and M. Kawade, "Ethnicity estimation with facial images". In Proc. 6th IEEE Int. Conf, on Automatic Face and Gesture Recognition (AFGR). pp. 195–200. (2004).

Hawkar O. Ahmed, Mahdi M. Younis and Shakhawan H. Wady. "LBP Variants as Texture Descriptors for Ethnicity Identification". JZS-A. Vol. 18. No.3. (2016). DOI: https://doi.org/10.17656/jzs.10550

Published

2020-12-20

How to Cite

Race Classification from Face Images Using Fast Fourier Transform and Discrete Cosine Transform. (2020). Journal of Zankoy Sulaimani - Part A, 22(2), 149-156. https://doi.org/10.17656/jzs.10816