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Title

A Method Based on Geometrical Features of Face and Using RB Neural Network to Classify People’s Age

Year: 1390
COI: IBBEC04_002
Language: EnglishView: 1,749
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Authors

Mehrdokht Bordbar - Electronic and Communication Department, Faculty of Electrical and Computer Engineering,Shiraz University, Shiraz, Iran
Mehran Yazdi - Electronic and Communication Department, Faculty of Electrical and Computer Engineering,Shiraz University, Shiraz, Iran
Sara Mardani Samani - Electronic and Communication Department, Faculty of Electrical and Computer Engineering,Shiraz University, Shiraz, Iran
Roya Mobaraki - Electronic and Communication Department, Faculty of Electrical and Computer Engineering,Shiraz University, Shiraz, Iran

Abstract:

A method for distinguishing people’s age based on their photo to the degree of stating the rate of their age is proposed. First the people’s face is extracted from their photo and then some features such as location of eyes, lip, nose, chin and forehead to each other is obtained. Finally by using RB neural network, age classification is made in three age ranges. The obtained results shows that incase the mentioned features are extracted correctly, RB neural network can fully determine three age ranges precisely. Comparing the results with two other classification methods including SVM and KNN indicates the primacy of RB neural network.

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Paper COI Code

This Paper COI Code is IBBEC04_002. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/162386/

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Bordbar, Mehrdokht and Yazdi, Mehran and Samani, Sara Mardani and Mobaraki, Roya,1390,A Method Based on Geometrical Features of Face and Using RB Neural Network to Classify People’s Age,4th Broadcasting Engineering Conference,Tehran,https://civilica.com/doc/162386

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  • W. B. Horng, C. P. Lee, C. W. Chen _ ...
  • _ H. Kwon, N. V. Lobot, "Age classification from facial ...

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Type of center: دانشگاه دولتی
Paper count: 21,764
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