Skip to main content
Top
Published in: Pattern Analysis and Applications 4/2019

25-05-2018 | Short Paper

CNN-based gender classification in near-infrared periocular images

Authors: Anirudh Manyala, Hisham Cholakkal, Vijay Anand, Vivek Kanhangad, Deepu Rajan

Published in: Pattern Analysis and Applications | Issue 4/2019

Log in

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

search-config
loading …

Abstract

Periocular region has emerged as a key biometric trait with potential applications in the forensics domain. In this paper, we explore two convolutional neural network (CNN)-based approaches for gender classification using near-infrared images of the periocular region. In the first stage, our approaches automatically detect and extract left and right periocular regions. The first approach utilizes a domain-specific pre-trained CNN to extract deep features from the periocular images. A trained support vector machine (SVM) then utilizes these features to predict the gender information. The second approach employs an end-to-end classifier obtained by fine-tuning a pre-trained CNN on the periocular images. Performance evaluations have been carried out on three databases, which includes an in-house and two public databases. Local binary pattern and histogram of oriented gradient-based methods have been used as baseline methods to ascertain the effectiveness of the proposed approaches. Our results indicate that the proposed approaches achieve higher classification accuracy than the baseline methods, particularly on one of the public databases that contains a large number of non-ideal images. In addition, accuracy of the proposed approaches is consistently higher than the existing eyebrow feature-based method.

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!

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!

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!

Literature
1.
go back to reference Merkow J, Jou B, Savvides M (2010) An exploration of gender identification using only the periocular region. In: IEEE international conference on biometrics: theory applications and systems, pp 1–5 Merkow J, Jou B, Savvides M (2010) An exploration of gender identification using only the periocular region. In: IEEE international conference on biometrics: theory applications and systems, pp 1–5
2.
go back to reference Lyle JR, Miller PE, Pundlik SJ, Woodard DL (2012) Soft biometric classification using local appearance periocular region features. Pattern Recogn 45:3877–3885CrossRef Lyle JR, Miller PE, Pundlik SJ, Woodard DL (2012) Soft biometric classification using local appearance periocular region features. Pattern Recogn 45:3877–3885CrossRef
3.
go back to reference Jain AK, Dass SC, Nandakumar K (2004) Can soft biometric traits assist user recognition? In: Defense and security, pp 561–572 Jain AK, Dass SC, Nandakumar K (2004) Can soft biometric traits assist user recognition? In: Defense and security, pp 561–572
4.
go back to reference Wang JG, Li J, Yau WY and Sung E (2010) Boosting dense sift descriptors and shape contexts of face images for gender recognition. In: Computer vision and pattern recognition—workshops, pp 96–102 Wang JG, Li J, Yau WY and Sung E (2010) Boosting dense sift descriptors and shape contexts of face images for gender recognition. In: Computer vision and pattern recognition—workshops, pp 96–102
5.
go back to reference Thomas V, Chawla NV, Bowyer KW, Flynn PJ (2007) Learning to predict gender from iris images. In: IEEE international conference on biometrics: theory, applications, and systems, pp 1–5 Thomas V, Chawla NV, Bowyer KW, Flynn PJ (2007) Learning to predict gender from iris images. In: IEEE international conference on biometrics: theory, applications, and systems, pp 1–5
6.
go back to reference Shafey LE, Khoury E, Marcel S (2014) Audio-visual gender recognition in uncontrolled environment using variability modeling techniques. In: IEEE international joint conference on biometrics, pp 1–8 Shafey LE, Khoury E, Marcel S (2014) Audio-visual gender recognition in uncontrolled environment using variability modeling techniques. In: IEEE international joint conference on biometrics, pp 1–8
7.
go back to reference Lee L, Grimson WEL (2002) Gait analysis for recognition and classification. In: IEEE international conference on automatic face and gesture recognition, pp 148–155 Lee L, Grimson WEL (2002) Gait analysis for recognition and classification. In: IEEE international conference on automatic face and gesture recognition, pp 148–155
8.
go back to reference Rattani A, Chen C, Ross A (2014) Evaluation of texture descriptors for automated gender estimation from fingerprints. In: European conference on computer vision. Springer, pp 764–777 Rattani A, Chen C, Ross A (2014) Evaluation of texture descriptors for automated gender estimation from fingerprints. In: European conference on computer vision. Springer, pp 764–777
9.
go back to reference Amayeh G, Bebis G, Nicolescu M (2008) Gender classification from hand shape. In: IEEE Computer Society conference on computer vision and pattern recognition workshops, pp 1–7 Amayeh G, Bebis G, Nicolescu M (2008) Gender classification from hand shape. In: IEEE Computer Society conference on computer vision and pattern recognition workshops, pp 1–7
10.
go back to reference Dantcheva A, Elia P, Ross A (2015) What else does your biometric data reveal? A survey on soft biometrics. IEEE Trans Inf Forensics Secur 11(3):1–26 Dantcheva A, Elia P, Ross A (2015) What else does your biometric data reveal? A survey on soft biometrics. IEEE Trans Inf Forensics Secur 11(3):1–26
11.
go back to reference Ross A, Chen C (2011) Can gender be predicted from near-infrared face images? In: Image analysis and recognition, pp 120–129 Ross A, Chen C (2011) Can gender be predicted from near-infrared face images? In: Image analysis and recognition, pp 120–129
12.
go back to reference Chen C, Ross A (2011) Evaluation of gender classification methods on thermal and near-infrared face images. In: International joint conference on biometrics (IJCB), pp 1–8 Chen C, Ross A (2011) Evaluation of gender classification methods on thermal and near-infrared face images. In: International joint conference on biometrics (IJCB), pp 1–8
13.
go back to reference Park U, Ross A, Jain AK (2009) Periocular biometrics in the visible spectrum: a feasibility study. In: Biometrics: theory, applications and systems, pp 1–6 Park U, Ross A, Jain AK (2009) Periocular biometrics in the visible spectrum: a feasibility study. In: Biometrics: theory, applications and systems, pp 1–6
14.
go back to reference Alonso-Fernandez F, Bigun J (2015) A survey on periocular biometrics research. Pattern Recogn Lett 82:92–105CrossRef Alonso-Fernandez F, Bigun J (2015) A survey on periocular biometrics research. Pattern Recogn Lett 82:92–105CrossRef
15.
go back to reference Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW et al (2005) Overview of the face recognition grand challenge. In: International conference on computer vision and pattern recognition, pp 947–954 Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW et al (2005) Overview of the face recognition grand challenge. In: International conference on computer vision and pattern recognition, pp 947–954
16.
go back to reference Castrillón-Santana M, Lorenzo-Navarro J, Ramón-Balmaseda E (2015) On using periocular biometric for gender classification in the wild. Pattern Recogn Lett 82:181–189CrossRef Castrillón-Santana M, Lorenzo-Navarro J, Ramón-Balmaseda E (2015) On using periocular biometric for gender classification in the wild. Pattern Recogn Lett 82:181–189CrossRef
17.
go back to reference Dong Y. and Woodard DL (2011) Eyebrow shape-based features for biometric recognition and gender classification: a feasibility study. In: International joint conference on biometrics (IJCB), pp 1–8 Dong Y. and Woodard DL (2011) Eyebrow shape-based features for biometric recognition and gender classification: a feasibility study. In: International joint conference on biometrics (IJCB), pp 1–8
18.
go back to reference Phillips PJ, Flynn PJ, Beveridge JR, Scruggs WT et al (2009) Overview of the multiple biometrics grand challenge. In: International conference on biometrics, pp 705–714 Phillips PJ, Flynn PJ, Beveridge JR, Scruggs WT et al (2009) Overview of the multiple biometrics grand challenge. In: International conference on biometrics, pp 705–714
19.
go back to reference Viola P (2004) Robust real-time face detection. Int J Comput Vis 57:137–154CrossRef Viola P (2004) Robust real-time face detection. Int J Comput Vis 57:137–154CrossRef
20.
go back to reference Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 806–813 Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 806–813
21.
go back to reference Ozbulak G, Aytar Y, Ekenel HK (2016) How transferable are CNN-based features for age and gender classification? In: International Conference of the Biometrics Special Interest Group (BIOSIG), pp 1–6 Ozbulak G, Aytar Y, Ekenel HK (2016) How transferable are CNN-based features for age and gender classification? In: International Conference of the Biometrics Special Interest Group (BIOSIG), pp 1–6
22.
go back to reference Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. In: British machine vision conference, pp 1–6 Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. In: British machine vision conference, pp 1–6
23.
24.
go back to reference Vapnik V (1998) Statistical learning theory. Wiley-Interscience, New YorkMATH Vapnik V (1998) Statistical learning theory. Wiley-Interscience, New YorkMATH
25.
go back to reference Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27CrossRef Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27CrossRef
27.
go back to reference Dago-Casas P, González-Jiménez D, Yu LL, Alba-Castro JL (2011) Single-and cross-database benchmarks for gender classification under unconstrained settings. In: IEEE international conference on computer vision workshops (ICCV workshops), pp 2152–2159 Dago-Casas P, González-Jiménez D, Yu LL, Alba-Castro JL (2011) Single-and cross-database benchmarks for gender classification under unconstrained settings. In: IEEE international conference on computer vision workshops (ICCV workshops), pp 2152–2159
28.
go back to reference Sharma A, Verma S, Vatsa M, Singh R (2014) On cross spectral periocular recognition. In: IEEE international conference on image processing (ICIP), pp 5007–5011 Sharma A, Verma S, Vatsa M, Singh R (2014) On cross spectral periocular recognition. In: IEEE international conference on image processing (ICIP), pp 5007–5011
29.
go back to reference Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987CrossRefMATH Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987CrossRefMATH
30.
go back to reference Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE conference on computer vision and pattern recognition, pp 886–893 Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE conference on computer vision and pattern recognition, pp 886–893
31.
32.
go back to reference Russakovsky O, Deng J, Su H, Krause J et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211–252MathSciNetCrossRef Russakovsky O, Deng J, Su H, Krause J et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211–252MathSciNetCrossRef
Metadata
Title
CNN-based gender classification in near-infrared periocular images
Authors
Anirudh Manyala
Hisham Cholakkal
Vijay Anand
Vivek Kanhangad
Deepu Rajan
Publication date
25-05-2018
Publisher
Springer London
Published in
Pattern Analysis and Applications / Issue 4/2019
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-018-0722-3

Other articles of this Issue 4/2019

Pattern Analysis and Applications 4/2019 Go to the issue

Premium Partner