Research Article
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Classification of Different Age Groups of People by Using Deep Learning

Year 2018, Volume: 7 Issue: 3, 9 - 16, 15.12.2018

Abstract



The Purpose of this study is to classify human images of
different age groups with VggNet which is one of the Deep Learning (DL) models.
Artificial intelligence, machine learning and computer vision have been carried
out in recent years at very advanced level. 
Undoubtedly, it is a great contribution of DL in the rapid progress of
these studies. Although DL foundational is based on past history, it has become
popular in the imageNet competition held in 2012. This is because the top-5 error
rate of 26.1% for visual object description has fallen to 15.3% for the first
time with a sharp decline that year with DL. The Convolution Neural Network
(CNN) is basis of DL models. It is basically composed of 4 layers. These are
Convolution Layer, ReLu Layer, Pooling Layer and Full Connected Layer. DL models
are designed using different numbers of these layers. In this study, people are
divided into 12 classes according to age groups. These classes are man, woman,
man face, woman face, old man, old woman, old man face, old woman face, boy,
girl, boy face, girl face respectively. A new data set was created for people
in 12 different age categories. For Each class 150 and totally 1800 images were
collected. 90% of these images were used for training and the remaining 10%
were used for testing. VggNet was trained with this data set. As a result of
the study, it was seen that people in different age groups were estimated with
78.5% accuracy with VggNet model. DL models need to be trained with large data
required. But it has been seen that training success has achieved a certain
value with little data.




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Year 2018, Volume: 7 Issue: 3, 9 - 16, 15.12.2018

Abstract

References

  • Ahmed, E., Jones, M., Marks, T.K., 2015. An improved deep learning architecture for person re-identification, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3908-3916.
  • Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., 2016. Deep speech 2: End-to-end speech recognition in english and mandarin, International Conference on Machine Learning, pp. 173-182.
  • Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., Bengio, Y., 2016. End-to-end attention-based large vocabulary speech recognition, Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE, pp. 4945-4949.
  • Bengio, Y., Courville, A., Vincent, P., 2013. Representation Learning: A Review and New Perspectives. Ieee T Pattern Anal 35, 1798-1828.
  • Deshpande, A., 2018. https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html.
  • Graves, A., Mohamed, A.-r., Hinton, G., 2013. Speech recognition with deep recurrent neural networks, Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, pp. 6645-6649.
  • Hermann, K.M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., Blunsom, P., 2015. Teaching machines to read and comprehend, Advances in Neural Information Processing Systems, pp. 1693-1701.
  • Heuritech, 2018. https://blog.heuritech.com/2016/02/29/a-brief-report-of-the-heuritech-deep-learning-meetup-5/.
  • Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29, 82-97.
  • Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., Wu, Y., 2016. Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410.
  • Krizhevsky, A., Sutskever, I., Hinton, G., 2012. ImageNet classification with deep convolutional neural networks. In NIPS’2012 . 23, 24, 27, 100, 200, 371, 456, 460.
  • Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C., 2016. Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360.
  • LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521, 436-444.
  • Lenz, I., Lee, H., Saxena, A., 2015. Deep learning for detecting robotic grasps. The International Journal of Robotics Research 34, 705-724.
  • Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., Quillen, D., 2016. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International Journal of Robotics Research, 0278364917710318.
  • Long, J., Shelhamer, E., Darrell, T., 2015. Fully convolutional networks for semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440.
  • Luong, M.-T., Pham, H., Manning, C.D., 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025.
  • Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016. You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788.
  • Ren, S., He, K., Girshick, R., Sun, J., 2015. Faster R-CNN: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, pp. 91-99.
  • Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Sun, Y., Wang, X., Tang, X., 2014. Deep learning face representation from predicting 10,000 classes, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891-1898.
  • Tian, Y., Luo, P., Wang, X., Tang, X., 2015. Pedestrian detection aided by deep learning semantic tasks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5079-5087.
  • Toshev, A., Szegedy, C., 2014. Deeppose: Human pose estimation via deep neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653-1660.
  • Yi, D., Lei, Z., Liao, S., Li, S.Z., 2014. Deep metric learning for person re-identification, Pattern Recognition (ICPR), 2014 22nd International Conference on. IEEE, pp. 34-39.
  • Zeng, X., Ouyang, W., Wang, X., 2013. Multi-stage contextual deep learning for pedestrian detection, Proceedings of the IEEE International Conference on Computer Vision, pp. 121-128.
There are 25 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Özkan İnik

Bülent Turan

Publication Date December 15, 2018
Published in Issue Year 2018 Volume: 7 Issue: 3

Cite

APA İnik, Ö., & Turan, B. (2018). Classification of Different Age Groups of People by Using Deep Learning. Journal of New Results in Science, 7(3), 9-16.
AMA İnik Ö, Turan B. Classification of Different Age Groups of People by Using Deep Learning. JNRS. December 2018;7(3):9-16.
Chicago İnik, Özkan, and Bülent Turan. “Classification of Different Age Groups of People by Using Deep Learning”. Journal of New Results in Science 7, no. 3 (December 2018): 9-16.
EndNote İnik Ö, Turan B (December 1, 2018) Classification of Different Age Groups of People by Using Deep Learning. Journal of New Results in Science 7 3 9–16.
IEEE Ö. İnik and B. Turan, “Classification of Different Age Groups of People by Using Deep Learning”, JNRS, vol. 7, no. 3, pp. 9–16, 2018.
ISNAD İnik, Özkan - Turan, Bülent. “Classification of Different Age Groups of People by Using Deep Learning”. Journal of New Results in Science 7/3 (December 2018), 9-16.
JAMA İnik Ö, Turan B. Classification of Different Age Groups of People by Using Deep Learning. JNRS. 2018;7:9–16.
MLA İnik, Özkan and Bülent Turan. “Classification of Different Age Groups of People by Using Deep Learning”. Journal of New Results in Science, vol. 7, no. 3, 2018, pp. 9-16.
Vancouver İnik Ö, Turan B. Classification of Different Age Groups of People by Using Deep Learning. JNRS. 2018;7(3):9-16.


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