Gender recognition using facial images
(Sprache: Englisch)
The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal...
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The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal facial features only. This system will classify the unknown facial images into male or female by comparing it with the images in the training set. The comparison will be done between most commonly used techniques for gender recognition that are the Genetic Algorithm (GA) and Support Vector Machine (SVM ) based on the facial features of a static image. Our results showed that our proposed SVM is better in detecting gender as compared to Genetic Algorithm.
Autoren-Porträt von Rubia Fatima, Affan Yasin, Abdul Basit Dogar
Fatima, RubiaRubia Fatima received her Master's degree in Information Technology (IT) from Bahauddin Zakariya University (B.Z.U), Multan, Pakistan in 2016. Currently, she is pursuing her Ph.D. in Software Engineering from School of Software, Tsinghua University, P.R.China. Her research specializes in cybersecurity and game-based education.
Bibliographische Angaben
- Autoren: Rubia Fatima , Affan Yasin , Abdul Basit Dogar
- 2019, 96 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 6200230765
- ISBN-13: 9786200230768
Sprache:
Englisch
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