吉林大学学报(工学版) ›› 2011, Vol. 41 ›› Issue (05): 1383-1387.

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Age estimation by extracting facial features of shape and texture

WANG Su-jing1, ZHOU Chun-guang1, ZHANG Na1, |LI Jian-peng2, ZHANG Li-biao1   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China|2.Telecommunication Engineering Institute, Air Force Engineering University, Xi'an 710077, China
  • Received:2010-07-11 Online:2011-09-01 Published:2011-09-01

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Abstract:

In this study Active Shape Model (ASM) was used to divide facial image into a number of triangles, and Local Binary Pattern (LBP) was applied to extract the texture feature of each triangle; then support vector regression was employed to estimate the age. Experiments were conducted on the FG-NET age database by Leave-One-Out strategy. The mean absolute error (MAE) and the CumScore were taken as the evaluation criteria. Results show that the MAEs of the proposed algorithm is smaller than that of the state-of-the-art algorithm, AGES, in both age estimation and age range estimation. Meanwhile, it was found that the texture around the mouth has little influence on age estimation.

Key words: artificial intelligence, facial recognition, age estimation, active shape model, local binary pattern, support vector regression

CLC Number: 

  • TP18
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