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Discover the age and gender of a person through deep learning

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Predicting the apparent age and gender from a picture is a very interesting problem from a technical point of view but can also be very useful when applied to better understand consumer segments or a user base for example. It can be used to infer the age or gender of a user and use this information to make personalized products and experiences for each user. In this post we will train a model to predict those attributes given a face picture.


In the medical profession, people rely mainly on the analysis of cholesterol, high density cholesterol, albumin, and other blood test indicators to determine a person’s “physiological age” and to study the degree of human aging. Unfortunately, this set of indicators is still very imperfect and of great inconvenience to use. If we can use computer and image processing technology to analyze facial images to accurately predict a person’s “physiological age” and compare “physiological age” and “actual age”, we can know whether you are in “Youth Permanent” or “Premature Aging”. Then it will greatly improve research efficiency and reduce research costs. By looking at the “face” to estimate the age, it can not only be used to quantify the aging, but also be applied to smart city and safe city construction. In everyday language conversation, the content and manner of conversation are often affected by other factors such as gender and age. For example, when faced with the elderly, the language of conversation will obviously be formal. More generally, humans quickly estimate each other’s gender, age, and identity through the appearance of the other person’s face in order to select different social styles.


Existing Method:

In existing Facial features used for gender classification are affected by their aging process, because human’s face is gradually changed as they grow up. Thus, in this paper, we propose a gender classification method robust to age variation by using age information and two facial features: appearance and geometry feature. Local Binary Patterns (LBP) is used as an appearance feature to classify gender of young and adult age group, and Euclidean distance among facial feature points is used as a geometry feature to classify gender of old age group. Experimental results showed that performance of our proposed method is increased about 2% compared to gender classification without using age information.


  • We can’t do it in real time here in existing.
  • Training is not proper for classification.
  • Accuracy is less

Proposed System:

  • Haar cascade file of face detection
  • Caffe model
  • Deep learning classification

Block Diagram:



  • Real time estimation
  • Improving classification accuracy


  • Finding lost children
  • Surveillance monitoring
  • Age factors effecting


Software Requirement:

  • Python Idle
  • Numpy
  • Opencv



[1] A Mike Burton, Vicki Bruce, What’s the difference between men and women? Evidence from facial measurement, Perception 22 (1993).

[2] Elizabeth Brown, What gives a face its gender?, Perception 22(1993).

[3] G. Guo, C. Dyer, Y. Fu, T. Huang, Is Gender Recognition Affected by Age?, In computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on 2009, pp. 2032-2039.

[4] C. Shan, Learning local binary patterns for gender classification on real-world face images, Pattern Recognition Letters 33 (4) (2012) 431-437.

[5] S. Choi, Y. Lee, S. Lee, K. Park, J. Kim, Age estimation using a hierarchical classifier based on global and local facial features, Pattern Recognition44 (2011) 1262–1281.

[6] A. Abaza, A. Ross, C. Hebert, M. A. F. Harrison, and M. S. Nixon. A survey on ear biometrics. ACM Computing Surveys, 45(2):22, 2013.

[7] G. Bradski and A. Kaehler. OpenCV. Dr. Dobbs Journal of Software Tools, 3, 2000.

[8] A. M. Bukar and H. Ugail. Automatic age estimation from facial profile view. IET Computer Vision, 11(8):650–655, 2017.

[9] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, pages 248–255. IEEE, 2009.

[10] Z. Emer ˇ siˇ c, V. ˇ Struc, and P. Peer. Ear recognition: More than ˇ a survey. Neurocomputing, 255:26–39, 2017.

[11] F. I. Eyiokur, D. Yaman, and H. K. Ekenel. Domain adaptation for ear recognition using deep convolutional neural networks. IET Biometrics, 7(3):199–206, 2017.

[12] P. Gnanasivam and S. Muttan. Gender classification using ear biometrics. In International Conference on Signal and Image Processing, pages 137–148. Springer, 2013.

[13] R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker. Multi-PIE. Image and Vision Computing, 28(5):807–813, 2010.

[14] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Computer Vision and Pattern Recognition, pages 770–778. IEEE, 2016.


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