التفاصيل البيبلوغرافية
| العنوان: |
Gender and race classification using geodesic distance measurement |
| المؤلفون: |
Marzoog, Zahraa Shahad, Hasan, Ashraf Dhannon, Abbas, Hawraa Hassan |
| المصدر: |
Indonesian Journal of Electrical Engineering and Computer Science, 27(2), 820-831, (2022-08-01) |
| بيانات النشر: |
Zenodo |
| سنة النشر: |
2022 |
| المجموعة: |
Zenodo |
| مصطلحات موضوعية: |
Ethnicity classification, Face recognition, Gender classification, Geodesic distance, Soft biometric |
| الوصف: |
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. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| Relation: |
oai:zenodo.org:7184169 |
| DOI: |
10.11591/ijeecs.v27.i2.pp820-831 |
| الاتاحة: |
https://doi.org/10.11591/ijeecs.v27.i2.pp820-831 |
| Rights: |
info:eu-repo/semantics/openAccess ; Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/legalcode |
| رقم الانضمام: |
edsbas.EF1944BD |
| قاعدة البيانات: |
BASE |