Paper Info
Title
Towards ethnicity detection using learning based classifiers
Abstract
Soft biometric such as gender, age, and ethnicity detection grows quickly as biometric system detection to support other solid biometric modality such as face, fingerprint, and iris. Nowadays, ethnicity detection plays an important role for pre-identify and re-identity people with different demographic background. The goal of this paper is ethnicity detection from ocular biometrics. In this work, two different level of fusion have been applied. The first level of fusion is feature fusion which fused two important local features which are Local Binary Pattern (LBP), and Histogram of Oriented Gradient (HOG). The second level of fusion have been made at the derision level for the best results of the classifiers. Moreover, four classifiers which are support vector machine (SVM), Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA) with overall ten kernels have been trained, validate, and tested on the FERET database to show the performance before and after fusion. The best result with an overall test performance accuracy of 98.5% using SVM with polynomial kernel that have been applied on the fused features of LBP and HOG.
Year
DOI
Venue
2017
10.1109/CEEC.2017.8101628
2017 9th Computer Science and Electronic Engineering (CEEC)
Keywords
Field
DocType
Biometric,Ethnicity Detection,Machine Learning,support vector machine,Multi-Layer Perceptron,Linear Discriminant Analysis,and Quadratic Discriminant Analysis
Pattern recognition,Computer science,Local binary patterns,Support vector machine,Polynomial kernel,Multilayer perceptron,Artificial intelligence,Linear discriminant analysis,FERET database,Perceptron,Machine learning,Quadratic classifier
Conference
ISSN
ISBN
Citations 
2472-1530
978-1-5386-3008-2
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Ahmad Saeed Mohammad101.01
Jabir Alshehabi Al-Ani200.68