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


  • 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




FFT, DCT, Fusion, Knn


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.


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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