ICTACT Journal on Soft Computing

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Deep Machine Learning for Age and Gender Prediction


Affiliations
1 Department of Electrical and Information Engineering, University of Nairobi, Kenya
     

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This paper tends to show that by learning feature representations through the employment of convolutional neural networks (CNN), a major increase in performance is obtained on age and gender prediction tasks. An image classifier is built in Matlab. Thousands of facial images are obtained and used to train a convolutional neural network. In this case of deep learning, the CNN essentially constructs abstract features from training image data, which would otherwise have to be handcraft in traditional machine learning model. Feeding in an image in an input, each layer it will perform a series of operations on that data until it outputs a label and classification percentage. Each layer has a different set of abstractions; in the first layers, the network basically teach itself edge detection, then shape detection in the middle layers. They get increasingly more abstract up until the end. The last few layers are the highest-level detectors for the whole object. A lot of computing power and time is spent to train the deep network. The trained network is then used to do predictions of age and gender and can, later after this paper, be integrated with webcam, at home or office to get statistical summary of all guests’ age and gender.

Keywords

Age, Gender, Prediction, Convolutional Neural Networks, Deep Machine Learning.
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  • Deep Machine Learning for Age and Gender Prediction

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Authors

George Wanga
Department of Electrical and Information Engineering, University of Nairobi, Kenya
Segera Rene Davies
Department of Electrical and Information Engineering, University of Nairobi, Kenya

Abstract


This paper tends to show that by learning feature representations through the employment of convolutional neural networks (CNN), a major increase in performance is obtained on age and gender prediction tasks. An image classifier is built in Matlab. Thousands of facial images are obtained and used to train a convolutional neural network. In this case of deep learning, the CNN essentially constructs abstract features from training image data, which would otherwise have to be handcraft in traditional machine learning model. Feeding in an image in an input, each layer it will perform a series of operations on that data until it outputs a label and classification percentage. Each layer has a different set of abstractions; in the first layers, the network basically teach itself edge detection, then shape detection in the middle layers. They get increasingly more abstract up until the end. The last few layers are the highest-level detectors for the whole object. A lot of computing power and time is spent to train the deep network. The trained network is then used to do predictions of age and gender and can, later after this paper, be integrated with webcam, at home or office to get statistical summary of all guests’ age and gender.

Keywords


Age, Gender, Prediction, Convolutional Neural Networks, Deep Machine Learning.

References