Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Optimal Quality of Information Service in an Electronic Commerce Website Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Cross-Age Face Recognition Using Deep Learning Model Based on Dual Attention Mechanism

Authors : Jialve Wang, Shenghong Li, Fucai Luo

Published in: Communications, Signal Processing, and Systems

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Although remarkable progresses have been made in the field of face recognition, the cross-age problem is still a huge challenge. The cross-age problem is mainly reflected in the fact that in addition to the unique identity features of each person, facial features also contain age features changing during aging. To address this problem, we propose a novel cross-age face recognition framework based on dual attention mechanism which combines residual-attention mechanism and self-attention mechanism. The introduction of attention mechanism makes the model focus more on identity features, ignoring the influence of age features. Extensive experiments are conducted on two well-known face aging datasets (MORPH and CACD) to show that the proposed method achieves notable improvement over state-of-the-art algorithms.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter The Radar Echo Extrapolation Based on ConvLSTM
next chapter Research on Classification of Alzheimer’s Disease Based on Multi-scale Features and Sequence Learning
Literature
1.
Chen B-C, Chen C-S, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. In: European conference on computer vision. Springer, pp 768–783
2.
3.
Fu J, Zheng H, Mei T (2017) Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4438–4446
4.
Gong D, Li Z, Tao D, Liu J, Li X (2015) A maximum entropy feature descriptor for age invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5289–5297
5.
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969
6.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
7.
Li Z, Park U, Jain AK (2011) A discriminative model for age invariant face recognition. IEEE Trans Inf For Secur 6(3):1028–1037CrossRef
8.
Ling H, Soatto S, Ramanathan N, Jacobs DW (2009) Face verification across age progression using discriminative methods. IEEE Trans Inf For Secur 5(1):82–91CrossRef
9.
Park U, Tong Y, Jain AK (2010) Age-invariant face recognition. IEEE Trans Pattern Anal Mach Intell 32(5):947–954CrossRef
10.
Ricanek K, Tesafaye T (2006) Morph: a longitudinal image database of normal adult age-progression. In: 7th international conference on automatic face and gesture recognition (FGR06). IEEE, pp 341–345
11.
Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823
12.
Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X (2017) Residual attention network for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3156–3164
13.
Wen Y, Li Z, Qiao Y (2016) Latent factor guided convolutional neural networks for age-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4893–4901
14.
Xiao-Lin L et al (2009) Analysis of morphous characteristics of facial reconstruction and the five organs in Chinese south five national minorities crowd. J Chongq Med Univ 10
15.
Xie S, Shan S, Chen X, Chen J (2010) Fusing local patterns of gabor magnitude and phase for face recognition. IEEE Trans Image Process 19(5):1349–1361MathSciNetCrossRef
16.
17.
Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig Process Lett 23(10):1499–1503CrossRef
18.
19.
Zheng T, Deng W, Hu J (2017) Age estimation guided convolutional neural network for age-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1–9
Metadata
Title
Cross-Age Face Recognition Using Deep Learning Model Based on Dual Attention Mechanism
Authors
Jialve Wang
Shenghong Li
Fucai Luo
Copyright Year
2021
Publisher
Springer Singapore
Book
Communications, Signal Processing, and Systems

Print ISBN: 978-981-15-8410-7

Electronic ISBN: 978-981-15-8411-4

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-8411-4_251