The prediction and classification of ethnicity based on iris texture patterns using image processing, artificial intelligence and computer vision techniques is still a recent topic in iris biometrics. While the large body of knowledge and research in iris biometrics has been focused on using iris features for individual recognition and verification, very little work has been focused on using iris texture patterns to determine other demographic attributes of individuals such as ethnicity and gender. The aim of this paper is to consolidate most of the work done by researchers, through delving and comparing the different techniques, algorithms and results achieved over the last decade.
Reference:
Mabuza-Hocquet, G., Nelwamondo, F. and Marwala, T. 2017. Ethnicity prediction and classification from iris texture patterns: A survey on recent advances. 2016 International Conference on Computational Science and Computational Intelligence, Las Vegas, NV, USA, 15-17 Dec 2016, p. 818-823. DOI: 10.1109/CSCI.2016.0159
Mabuza-Hocquet, G., Nelwamondo, F. V., & Marwala, T. (2017). Ethnicity prediction and classification from iris texture patterns: A survey on recent advances. IEEE. http://hdl.handle.net/10204/9460
Mabuza-Hocquet, Gugulethu, Fulufhelo V Nelwamondo, and T Marwala. "Ethnicity prediction and classification from iris texture patterns: A survey on recent advances." (2017): http://hdl.handle.net/10204/9460
Mabuza-Hocquet G, Nelwamondo FV, Marwala T, Ethnicity prediction and classification from iris texture patterns: A survey on recent advances; IEEE; 2017. http://hdl.handle.net/10204/9460 .
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