Hybrid Wavelet and Discrete Cosine Transform Methods for Ethnicity Identification

Hybrid Wavelet and Discrete Cosine Transform Methods for Ethnicity Identification

Faraidoon H. Ahmad1 & Aree A. Mohammed2

1  College of Commerce Dept. of Computer & Statistics. University of Sulaimani, Sulaimani, Kurdistan Region, Iraq. 

2 Department of Computers , School of Science, Faculty of Science and Science Educations, University of Sulaimani, Kurdistan Region, Iraq.

e-mail: faraidoon1976@yahoo.com

Article info
Original: 28 Oct. 2014 
Revised: 02 Nov. 2014 
Accepted: 10 Dec. 2014
Published online: 20 March 2015

Key Words:
Hybrid Scheme
Discrete Cosine Transform (DCT)
Discrete Wavelet Transform (DWT)
Global Feature Extractor K Nearest Neighbor (K-NN)


Ethnic (Race) denotes to people who share common facial features that perceptually discriminate them from members of other ethnic groups. Ethnicity identification from face images is a process of facial features compilation of an individual compared to existing faces to inference his/her ethnic class, it play important role in face-related applications. In this paper, an improved ethnicity identification system’s accuracy is proposed through hybrid Wavelet and DCT (Discrete Cosine Transform) global feature extractor. Firstly, a wavelet transform is applied to the face image with 4 levels of decomposition. And then the DCT transform is performed on the LL4 (Low-Low) approximation coefficients band. A frontal facial database containing (950) images of three different ethnic groups (European, Oriental, and African) with different conditions (lighting, expression, with glasses or without) is used for experimental tests. Results show that the proposed scheme is outperform other recent related works in terms of accuracy and efficiency.

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Kewan Omer,
May 29, 2015, 12:16 PM