Abstract
Human gender is an important demographic characteristic in the society. Recognizing demography characteristics of individuals, for example, age and gender using automatic image recognition taken much consideration in last few years. This paper proposes the extraction of geometric and appearance feature of face automatically from the front view. For extracting the feature, cumulative benchmark approach is used. Two basic categories as supervised as well as unsupervised methodology may be applied for gender grouping. In this paper, we used supervised machine learning approach. We have used three diverse classifiers, for this approach as SVM, neural network, and adobos. We have trained all the classifiers by means of identical training dataset and similar feature. We have done a comparative study of the performance of these classifiers and which classifier is best for our primary dataset over face images.
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Verma, V.K., Srivastava, S., Jain, T., Jain, A. (2019). Local Invariant Feature-Based Gender Recognition from Facial Images. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_69
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DOI: https://doi.org/10.1007/978-981-13-1595-4_69
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