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A software which can recognize the age and gender of the face in a photo or video, and carry out precision marketing.

  • Implement the model in the provided paper and adjust parameters for a higher accuracy.
  • Improve the network architecture in Caffe.
  • Create a interface in Caffe for C#.
  • Implement the software and UI, and introduce advertisements based on predicted age and gender.

Existing Approach

  • Based on accurate localization of facial features [1], [2].
  • Neural network trained on near-frontal face images [3].
  • Deep convolutional neural networks (CNN) [4].
  • CNN network architecture in provided paper [5].

Proposed Idea

  • Modify on the base of [5].
  • VGG model (ImageNet ILSVRC-2014), 16-layer [6].

Experimental Results

Classifier Method Accuracy
Age Best in [5] 50.7
Age Ours 69.0
Gender Best in [5] 86.8
Gender Ours 95.2


[1] Y. H. Kwon and N. da Vitoria Lobo. Age classification from facial images. In Proc. Conf. Comput. Vision Pattern Recognition, pages 762–767. IEEE, 1994. 1, 2

[2] L. Rabiner and B.-H. Juang. An introduction to hidden markov models. ASSP Magazine, IEEE, 3(1):4–16, 1986. 2

[3] B. A. Golomb, D. T. Lawrence, and T. J. Sejnowski. Sexnet: A neural network identifies sex from human faces. In Neural Inform. Process. Syst., pages 572–579, 1990. 2

[4] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Neural Inform. Process. Syst., pages 1097–1105, 2012. 3, 4

[5] Gil Levi and Tal Hassner, Age and Gender Classification using Convolutional Neural Networks, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, June 2015

[6] K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv technical report, 2014

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