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Title: Gender classification
Authors: Abi Fadel, Fady Azar
Advisors: Dagher, Issam 
Subjects: Human face recognition (Computer science)
Issue Date: 2017
Gender recognition has been playing a very important role in various applications such as human computer interaction, surveillance and security. In this context, this project aims to investigate a gender classification system and propose new models for a better performance. We have evaluated different learning algorithm; the SVM – RBF with a 5% outlier fraction outperformed other classifiers. We have examined the effectiveness of different feature selection methods. We have proposed two classification methods that focus on training subsets of images among the training images. Method 1 combines the outcome of different classifiers based on different image subsets, while method 2 is based on clustering the training data and building a classifier for each cluster. Experimental results showed that both methods might increase the classification accuracy. The first method performed better than the second one. In addition, results have shown that method 1 can reach 100% accuracy.
Includes bibliographical references (p. 43-45).

Supervised by Dr. Issam Dagher.
Rights: This object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holder
Ezproxy URL: Link to full text
Type: Project
Appears in Collections:UOB Theses and Projects

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