Gender and Age Estimation Using Face Images
نویسنده
چکیده
In this study, I present a real-time gender and age estimation approach that I developed for the course project of Artificial Neural Networks. For these tasks, I employed a local appearance-based representation using Discrete Cosine Transform (DCT), which has been shown to be very effective in realtime processing and robust against changes caused by light and facial expressions. Using these features, I trained support vector machine classifiers. Binary support vector machine classifier is used to discriminate between males and females. In case of age estimation, a two-step classifier is used. First, a support vector machine classifier is used to discriminate between youths and adults. Then, three separate support vector machine regression functions are used for youths, adults and for those that are close to the youth-adult decision border.
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