Abstract
Recognizing human gender automatically by a computer is a challenging problem, which attracts research attention due to its vast real-life application. Gender classification has been playing a wide role in security and surveillance system. The proposed system uses wavelet and Radon transforms for features extraction and analyzed with support vector machine (SVM) as classifier. Experimental analysis shows that recommend system gives the better results for neutral, expression, and partially occluded face images. Obtained results indicate that this gender classification system gives an effective classification rate on FERET and AR datasets under conditional and unconditional environments.
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Sangha, R.K., Rai, P. (2019). An Appearance-Based Gender Classification Using Radon Features. In: Shukla, R.K., Agrawal, J., Sharma, S., Singh Tomer, G. (eds) Data, Engineering and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-6347-4_15
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