Gender and race classification using geodesic distance measurement

Zahraa Shahad Marzoog, Ashraf Dhannon Hasan, Hawraa Hassan Abbas


Gender and ethnicity classifications are a long-standing challenge in the face recognition’s field. They are key-demographic traits of individuals and applied in real-world applications such as biometric and demographic research, human-computer interaction (HCI), law enforcement and online advertisements. Thus, many methods have been proposed to address gender or/and race classifications and achieved various accuracies. This research improves race and gender classification by employing a geodesic path algorithm to extract discriminative features of both gender and ethnicity. PCA is also utilized for dimensionality reduction of Gender-feature and race-feature matrices. KNN and SVM are used to classify the extracted feature. This research was tested on the face recognition technology (FERET) dataset, with classification results demonstrating high-level performance (100%) in distinguishing gender and ethnicity.


Ethnicity classification; Face recognition; Gender classification; Geodesic distance; Soft biometric;

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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