<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.</p></abstract>

Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.

Learned Features are Better for Ethnicity Classification

Intelligent Systems Research Institute (ISRI), College of Information and Communication Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Learned Features are Better for Ethnicity Classification"}

Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications...

Learned Features are Better for Ethnicity Classification

Publié en ligne: 04 oct. 2017

Pages: 152 - 164

DOI: https://doi.org/10.1515/cait-2017-0036

Mots clés
Ethnicity recognition, race classification, Convolutional Neural Network (CNN), VGG Face, Support Vector Machine (SVM)

© 2017 Inzamam Anwar et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Learned Features are Better for Ethnicity Classification

Publié en ligne: 04 oct. 2017

Pages: 152 - 164

DOI: https://doi.org/10.1515/cait-2017-0036

Mots clésEthnicity recognition, race classification, Convolutional Neural Network (CNN), VGG Face, Support Vector Machine (SVM)

© 2017 Inzamam Anwar et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Ethnicity recognition, race classification, Convolutional Neural Network (CNN), VGG Face, Support Vector Machine (SVM)