Gender and race classification using geodesic distance measurement

Zahraa Shahad Marzoog (Kerbala University)
Ashraf Dhannon Hasan (Kerbala University)
Hawraa Hassan Abbas (Kerbala University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 27, No 2: August 2022

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.