Merve Kilinc Yildirim

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Face recognition approaches that are based on deep convolutional neural networks (CNN) have been dominating the field. The performance improvements they have provided in the so called in-the-wild datasets are significant, however, their performance under image quality degradations have not been assessed, yet. This is particularly important, since in(More)
Aging progress of a person is influenced by many factors such as genetics, health, lifestyle, and even weather conditions. Therefore human age estimation from a face image is a challenging problem. Aging causes significant variations in facial shape and texture across years. In order to construct a general age classifier, shape and texture information of(More)
In this study, an approach is developed for automatic gender classification from human face images. For representing the face images, different kinds of feature extraction methods (Local Binary Pattern operator, Gabor filtering, Local Gabor Binary Pattern operator) are used. The feature vectors are tested with AdaBoost and Support Vector Machine (SVM)(More)
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