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A new biologically inspired active appearance model for face age estimation by using local ordinal ranking

Published: 17 August 2013 Publication History

Abstract

In this paper, a new facial feature called Biologically Inspired Active Appearance Model (BIAAM) is proposed for face age estimation by using a novel age function learning algorithm, called Local Ordinal Ranking (LOR). In BIAAM, appearance variations are encoded by extracting Bio Inspired Feature from normalized shape-free images with a mean shape mask. The proposed LOR divides the training set into several groups according to age labels and applies Ordinal Hyperplanes Ranker for each group to determine the final predicting age. A multiple linear regression function is used to decide which group a query sample belongs to. Experimental evaluation on the FG-NET aging database with mean absolute error 4.18 years demonstrates that our method outperforms other state-of-the-art algorithms.

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Cited By

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  • (2017)Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age EstimationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2017.269545612:11(2505-2517)Online publication date: Nov-2017
  • (2017)An All-In-One Convolutional Neural Network for Face Analysis2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)10.1109/FG.2017.137(17-24)Online publication date: May-2017
  • (2016)A cascaded convolutional neural network for age estimation of unconstrained faces2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)10.1109/BTAS.2016.7791154(1-8)Online publication date: Sep-2016
  • Show More Cited By

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  1. A new biologically inspired active appearance model for face age estimation by using local ordinal ranking

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      cover image ACM Other conferences
      ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
      August 2013
      419 pages
      ISBN:9781450322522
      DOI:10.1145/2499788
      • Conference Chair:
      • Tat-Seng Chua,
      • General Chairs:
      • Ke Lu,
      • Tao Mei,
      • Xindong Wu
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      • NSF of China: National Natural Science Foundation of China
      • University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
      • Beijing ACM SIGMM Chapter

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 17 August 2013

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      Author Tags

      1. active appearance model
      2. biologically inspired feature
      3. face age estimation
      4. machine learning

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      • Research-article

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      ICIMCS '13
      Sponsor:
      • NSF of China
      • University of Sciences & Technology, Hefei

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      ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;
      Overall Acceptance Rate 163 of 456 submissions, 36%

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      Cited By

      View all
      • (2017)Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age EstimationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2017.269545612:11(2505-2517)Online publication date: Nov-2017
      • (2017)An All-In-One Convolutional Neural Network for Face Analysis2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)10.1109/FG.2017.137(17-24)Online publication date: May-2017
      • (2016)A cascaded convolutional neural network for age estimation of unconstrained faces2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)10.1109/BTAS.2016.7791154(1-8)Online publication date: Sep-2016
      • (2016)Age Estimation by LS-SVM Regression on Facial ImagesAdvances in Visual Computing10.1007/978-3-319-50832-0_36(370-379)Online publication date: 10-Dec-2016
      • (2015)An Overview of Research Activities in Facial Age Estimation Using the FG-NET Aging DatabaseComputer Vision - ECCV 2014 Workshops10.1007/978-3-319-16181-5_56(737-750)Online publication date: 20-Mar-2015

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