International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Gender Classification using OpenCV and Feature Extraction

Abstract

Face detection [1] is not an easy task for machines like it is for humans. The machines need to be trained thoroughly with many data, because the machines cannot understand by its own. When we the humans look at someone’s face, we may decode much information about the person just by looking at the face such as gender, age and sometimes behavior or calmness also. Our brain is also trained for recognizing gender from a face. The aim of this project is to appropriately train the machine using algorithms so that it can identify and detect the differences between male and female faces. The pixel and the data of the image are together known as the picture element. It is the compact most element of an image. Both the processes face recognition and face detection, mainly deal with the pixels and their respective picture elements. The attributes of the image or based upon the features extracted [2] from each image can be used for the face recognition.

Country : India

1 Dibyendu Saha2 Anisha Banik3 Koushik Bhattacharyya

  1. B.Tech Student, Computer Science Engineering, Dream Institute of Technology, Kolkata, India
  2. B.Tech Student, Computer Science Engineering, Dream Institute of Technology, Kolkata, India
  3. Assistant Professor, Computer Science Engineering, Dream Institute of Technology, Kolkata, India

IRJIET, Volume 4, Issue 4, April 2020 pp. 36-40

doi.org/10.47001/IRJIET/2020.404005

Full Paper
Download

References

  1. Face detection and its applications, Mrs. Sunita Roy1
  2. A Comparative Study of Feature Extraction Methods in Images Classification, Seyyid Ahmed Medjahed
  3. Machine Learning Algorithms: A Review, AyonDey
  4. Various Biometric Authentication Techniques: A Review, J BiomBiostat
  5. Human-Computer Interaction in Smart Environments, GianlucaParavati
  6. Comparison between YCbCr Color Space and CIELab Color Space for Skin Color Segmentation, Amanpreet, Kaur
  7. Facial Recognition using OpenCV, Shervin EMAMI1
  8. Object detection using Haar-cascade Classifier, Sander Soo
  9. Explaining AdaBoost, Robert E. Schapire
  10. Rapid Object Detection using a Boosted Cascade of Simple Features, Paul Viola
  11. Light compensation, Chih-Fong Chang
  12. Face Recognition, Wei-Lun Chao
  13. Fast Face Detection with Multi-Scale Window Search Free from Image Resizing Using SGI Features, Masayuki Miyama
  14. A Real generalization of discrete AdaBoost, Richard Nock
  15. Omni directional face detection based on Real Adaboost, Chang Huang
  16. An efficient gentle Adaboost based approach for Mammograms classification, NezhaHamdi
  17. Additive logistic regression: a statistical view of boosting, Jerome Friedman
  18. A Review Paper on Gabor Filter Algorithm & Its Applications, Neelu Arora
  19. Gaussian Probability Density Functions: Properties and Error Characterization, Maria Isabel Ribeiro
  20. An efficient Radix-two Algorithm to compute the 2D Fourier Transform, D. CHIKOUCHE
  21. Object detection using Haar-cascade Classifier, Sander Soo
  22. Data classification using Support Vector Machine, Durgesh Srivastava
  23. A Practical Guide to Support Vector Classification, Chih-Wei Hsu
  24. A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection, Muhammad Achirul Nanda
  25. Efficient Sampling of SAT Solutions for Testing, Rafael Dutra
  26. The CMU Pose, Illumination, and Expression Database, Terence Sim
  27. Introduction to Augmented Reality, R. Silva
  28. The FERET evaluation methodology for face recognition algorithms, P. Jonathon Phillips

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies