Gender Classification System Based on Deep Learning in Low Power Embedded Board


KIPS Transactions on Software and Data Engineering, Vol. 6, No. 1, pp. 37-44, Jan. 2017
10.3745/KTSDE.2017.6.1.37,   PDF Download:
Keywords: Gender Classification, Deep Learning, Embedded Board, low power
Abstract

While IoT (Internet of Things) industry has been spreading, it becomes very important for object to recognize user’s information by itself without any control. Above all, gender (male, female) is dominant factor to analyze user’s information on account of social and biological difference between male and female. However since each gender consists of diverse face feature, face-based gender classification research is still in challengeable research field. Also to apply gender classification system to IoT, size of device should be reduced and device should be operated with low power. Consequently, To port the function that can classify gender in real-world, this paper contributes two things. The first one is new gender classification algorithm based on deep learning and the second one is to implement real-time gender classification system in embedded board operated by low power. In our experiment, we measured frame per second for gender classification processing and power consumption in PC circumstance and mobile GPU circumstance. Therefore we verified that gender classification system based on deep learning works well with low power in mobile GPU circumstance comparing to in PC circumstance.


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Cite this article
[IEEE Style]
H. Jeong, D. H. Kim, W. J. Baddar, Y. M. Ro, "Gender Classification System Based on Deep Learning in Low Power Embedded Board," KIPS Transactions on Software and Data Engineering, vol. 6, no. 1, pp. 37-44, 2017. DOI: 10.3745/KTSDE.2017.6.1.37.

[ACM Style]
Hyunwook Jeong, Dae Hoe Kim, Wisam J. Baddar, and Yong Man Ro. 2017. Gender Classification System Based on Deep Learning in Low Power Embedded Board. KIPS Transactions on Software and Data Engineering, 6, 1, (2017), 37-44. DOI: 10.3745/KTSDE.2017.6.1.37.