SavedAdd your journal

Journals / Journal of Naval Sciences and Engineering / 2017 / Cilt: 13 - Sayı: 1

GENDER CLASSIFICATION FROM FACE IMAGES

Eyyüp YILDIZ
Tolga ENSARİ
Pages
31–42
Publication Date
2017-04-28
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the experiments, defined as hybrid method principal component analysis with k-nearest neighbor method gives better recognition accuracy then defined as hybrid method principal component analysis with support vector machine method.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this article, we study on gender classification which is one of the important issue in security, statistics and related commercial areas.
📊
Key Findings & Data:In the study, FEI face data set has been used that has 200 female and 200 male frontal face images.
💡
Conclusion & Impact:According to the experiments, defined as hybrid method principal component analysis with k-nearest neighbor method gives better recognition accuracy then defined as hybrid method principal component analysis with support vector machine method.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Gender classification · face recognition · principal component analysis

Abstract

In this article, we study on gender classification which is one of the important issue in security, statistics and related commercial areas. In the study, FEI face data set has been used that has 200 female and 200 male frontal face images. Principal component analysis (PCA) has been used for feature extraction process. We use all part of the face images instead of taking some part of them. Support Vector Machine (SVM) and k-nearest neighbor algorithms used for classification test phases. We compare the results which obtained in our experiments and give them in tables and graphs. According to the experiments, defined as hybrid method principal component analysis with k-nearest neighbor method gives better recognition accuracy then defined as hybrid method principal component analysis with support vector machine method.

🔖

Cite This Article

YILDIZ, E., ENSARİ, T. (2017). GENDER CLASSIFICATION FROM FACE IMAGES. Journal of Naval Sciences and Engineering, Cilt: 13 - Sayı: 1, 31–42. https://ajindex.com/articles/238136

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (16)

  1. Hotelling, H.: Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology, vol. 24, (1933), 417–441 and 498–520
  2. Alpaydın E.: Introduction to machine learning, The MIT Press, USA, (2015)
  3. Pearson, K.: On lines and planes of closest fit to systems of points in space, Philosophical Magazine, vol. 2, no.11, (1901), 559–572
  4. Jain, A. ve Huang, J.: Integrating independent components and linear discriminant analysis for gender classification, International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, (2004), 159-163
  5. U. Turhal, A. Akbaş: Yüz örüntülerinden cinsiyet tespitinde hibrit özniteliklerle performans geliştirme, Elektrik-Elektronik ve Bilgisayar Sempozyumu, (2016), 361-365
  6. Z. Sun, G. Bebis: X. Yuan, S. J. Louis, Genetic Feature Subset Selection for Gender Classification: A comparison study, IEEE Workshop on Applications of Computer Visions, (2002), 165-170
  7. L. Lu, P. Shi: A novel fusion-based method for expression-invariant gender classification, IEEE International Conference on Acoustic, Speech and Signal Processing, (2009) 1065-1068
  8. H. Hasassnpour, H. Dehghan: Neonate facial gender classification using PCA and fuzzy clustering, Iranian Conference of Biomedical Eng., (2010), 1-5
  9. M. Hu, Y. Zheng, F. Ren, H. Jiang: Age estimation and gender classification of facial images based on local directional pattern, IEEE International Conference on Cloud Computing and Intelligent Systems, (2014), 103-107
  10. A. Verma, L. Vig: Using Convolutional Neural Networks to discover cognitively validated features for Gender Classification, International Conference on Soft Computing and Machine Intelligence, (2014), 33-37
  11. T. Bissoon, S. Viriri: Gender classification using Face Recognition, International Conference on Adaptive Science and Technology, (2013), 1-4
  12. S. A. Khan, M. Nazir, N. Naveed, N. Riaz: Efficient gender classification methodology using DWT and PCA, IEEE International Conference on Multitopic Conference, (2011), 155-158
  13. R. Brunelli, T. Poggio: Hyber BF networks for gender classification, DARPA Image Understanding Workshop, (1992)
  14. Pearson, K.: On lines and planes of closest fit to systems of points in space, Philosophical Magazine 2 (6), (1901), 559-572
  15. Fix, E., Hodges, J. L.: Discriminatory analysis nonparametric discrimination: Consistency properties, Technical Report 4, (1951)
  16. Wiesner V., Evers L.: Statistical