A Comparative Study of Soft Biometric Traits and Fusion Systems for Face-based Person Recognition

Full Text (PDF, 422KB), PP.45-53

Views: 0 Downloads: 0

Author(s)

Samuel Ezichi 1,* Ijeoma J.F. Ezika 1 Ogechukwu N. Iloanusi 1

1. Dept. of Electronic Engineering, University of Nigeria Nsukka, Enugu State Nigeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2021.06.05

Received: 6 Apr. 2021 / Revised: 5 Aug. 2021 / Accepted: 19 Sep. 2021 / Published: 8 Dec. 2021

Index Terms

Soft biometrics, biometric fusion, face recognition

Abstract

Soft biometrics is not a unique trait in itself, but it is valuable in enhancing the performance of unique traits used in biometric recognition systems. In this paper, we perform a comparative analysis of soft biometric traits and fusion schemes for improving face recognition systems. Specifically, we present an analysis of the performance of such systems as a function of the fusion strategy used and the soft biometric feature employed. We outline the strengths and weaknesses of the biometric feature employed in fused face and soft biometric systems. The analysis presented in this work is significantly important and different from existing works as the performance profiles of a wider variety of soft biometric traits are compared over major metrics of permanence, ease of collection and distinctiveness. 

Cite This Paper

Samuel Ezichi, Ijeoma J.F. Ezika, Ogechukwu N. Iloanusi, " A Comparative Study of Soft Biometric Traits and Fusion Systems for Face-based Person Recognition", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.13, No.6, pp. 45-53, 2021. DOI: 10.5815/ijigsp.2021.06.05

Reference

[1]A. Dantcheva, C. Velardo, A. D’Angelo, and J. L. Dugelay, “Bag of soft biometrics for person identification: New trends and challenges,” Multimed. Tools Appl., vol. 51, no. 2, pp. 739–777, 2010.

[2]K. Nandakumar, “Multibiometric Systems : Fusion Strategies and Template Security,” Michigan State Univ East Lansing Dept of Computer Science/Engineering, 2008.

[3]P. S. Sanjekar and J. B. Patil, “Wavelet based multimodal biometrics with score level fusion using mathematical normalization,” International Journal of Image, Graph. Signal Process., vol. 11, no. 4, p. 63, 2019.

[4]U. Park and A. K. Jain, “Face matching and retrieval using soft biometrics,” IEEE Trans. Inf. Forensics Secur., vol. 5, no. 3, pp. 406–415, 2010.

[5]A. Dantcheva, P. Elia, and A. Ross, “What else does your biometric data reveal? A survey on soft biometrics,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 3, pp. 441–467, 2015.

[6]A. El Kissi Ghalleb, S. Sghaier, and N. Essoukri Ben Amara, “Face recognition improvement using soft biometrics,” in 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013, 2013, pp. 1–6.

[7]A. Halleluyah Oluwatobi and O. O.F.W., “A Soft Computing Model of Soft Biometric Traits for Gender and Ethnicity Classification,” International Journal of Engineering Manufacturing, vol. 9, no. 2, pp. 54–63, 2019.

[8]M. Ghayoumi, “A review of multimodal biometric systems: Fusion methods and their applications,” in 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), 2015, pp. 131–136.

[9]M. O. Oloyede and G. P. Hancke, “Unimodal and multimodal biometric sensing systems: a review,” IEEE Access, vol. 4, pp. 7532–7555, 2016.

[10]Y. Fu, G. Guo, and T. S. Huang, “Age synthesis and estimation via faces: A survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 11, pp. 1955–1976, 2010.

[11]O. A. Arigbabu, S. M. S. Ahmad, W. A. W. Adnan, and S. Yussof, “Integration of multiple soft biometrics for human identification,” Pattern Recognit. Lett., vol. 68, pp. 278–287, 2015.

[12]A. Eleyan, “Enhanced face recognition using data fusion,” International Journal of Intelligent Systems & Applications, vol. 5, p. 98, 2012.

[13]D. A. Reid and M. S. Nixon, “Using comparative human descriptions for soft biometrics,” in 2011 International Joint Conference on Biometrics, IJCB 2011, 2011.

[14]H. Zhang, J. R. Beveridge, B. A. Draper, and P. J. Phillips, “On the effectiveness of soft biometrics for increasing face verification rates,” Comput. Vis. Image Underst., vol. 137, pp. 50–62, 2015.

[15]T. Djara, A. A. Sobabe, and A. Vianou, “Incorporating metadata in multibiometric score-level fusion: An optimized architecture,” Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 1, pp. 5290–5305, 2019.

[16]Z. Song, M. Wang, X. Hua, and S. Yan, “Predicting occupation via human clothing and contexts,” in 2011 International Conference on Computer Vision, 2011, pp. 1084–1091.

[17]A. K. Jain, S. C. Dass, and K. Nandakumar, “Soft biometric traits for personal recognition systems,” in International conference on biometric authentication, 2004, pp. 731–738.

[18]P. Tome, J. Fierrez, R. Vera-Rodriguez, and M. S. Nixon, “Soft Biometrics and Their Application in Person Recognition at a Distance,” IEEE Trans. Inf. FORENSICS Secur., vol. 9, no. 3, pp. 464–475, 2014.

[19]J.-E. Lee, A. K. Jain, and R. Jin, “Scars, marks and tattoos (SMT): Soft biometric for suspect and victim identification,” in 2008 Biometrics symposium, 2008, pp. 1–8.

[20]H. Proença and J. C. Neves, “Soft biometrics: Globally coherent solutions for hair segmentation and style recognition based on hierarchical mrfs,” IEEE Trans. Inf. Forensics Secur., vol. 12, no. 7, pp. 1637–1645, 2017.

[21]N. Almudhahka, M. Nixon, and J. Hare, “Human face identification via comparative soft biometrics,” in 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), 2016, pp. 1–6.

[22]D. R. Mane, A. D. Kale, M. B. Bhai, and S. Hallikerimath, “Anthropometric and anthroposcopic analysis of different shapes of faces in group of Indian population: a pilot study,” J. Forensic Leg. Med., vol. 17, no. 8, pp. 421–425, 2010.

[23]P. K. Chattopadhyay and S. Bhatia, “Morphological examination of ear: a study of an Indian population,” Leg. Med., vol. 11, pp. S190--S193, 2009.

[24]A. Dantcheva, N. Erdogmus, and J.-L. Dugelay, “On the reliability of eye color as a soft biometric trait,” in 2011 IEEE Workshop on Applications of Computer Vision (WACV), 2011, pp. 227–231.

[25]A. Kuehlkamp, B. Becker, and K. Bowyer, “Gender-from-iris or gender-from-mascara?,” in 2017 IEEE Winter conference on applications of computer vision (WACV), 2017, pp. 1151–1159.

[26]A. Moorhouse, A. N. Evans, G. A. Atkinson, J. Sun, and M. L. Smith, “The nose on your face may not be so plain: Using the nose as a biometric,” in 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009), 2009, no. 2.

[27]E. Gonzalez-Sosa, J. Fierrez, R. Vera-Rodriguez, and F. Alonso-Fernandez, “Facial soft biometrics for recognition in the wild: Recent works, annotation, and COTS evaluation,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 8, pp. 2001–2014, 2018.

[28]C. Chen, A. Dantcheva, and A. Ross, “Automatic facial makeup detection with application in face recognition,” in 2013 international conference on biometrics (ICB), 2013, pp. 1–8.

[29]S. Samangooei, B. Guo, and M. S. Nixon, “The use of semantic human description as a soft biometric,” in 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems, 2008, pp. 1–7.

[30]H. Chen, A. Gallagher, and B. Girod, “Describing clothing by semantic attributes,” in European conference on computer vision, 2012, pp. 609–623.

[31]A. K. Jain, K. Nandakumar, X. Lu, and U. Park, “Integrating faces, fingerprints, and soft biometric traits for user recognition,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3087, no. May. pp. 259–269, 2004.

[32]M. C. D. C. Abreu and M. Fairhurst, “Enhancing identity prediction using a novel approach to combining hard-and soft-biometric information,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 41, no. 5, pp. 599–607, 2010.

[33]E. S. Jaha, “Augmenting Gabor-based face recognition with global soft biometrics,” in 7th International Symposium on Digital Forensics and Security, ISDFS 2019, 2019, pp. 1–5.

[34]Y. Wang and M. Kosinski, “Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.,” J. Pers. Soc. Psychol., vol. 114, no. 2, p. 246, 2018.

[35]E. S. Jaha and M. S. Nixon, “Soft biometrics for subject identification using clothing attributes,” in IEEE international joint conference on biometrics, 2014, pp. 1–6.

[36]G. Guo, G. Mu, and K. Ricanek, “Cross-age face recognition on a very large database: The performance versus age intervals and improvement using soft biometric traits,” Proc. - Int. Conf. Pattern Recognit., pp. 3392–3395, 2010.

[37]K. G. Srinivasa and S. Gosukonda, “Continuous multimodal user authentication: coupling hard and soft biometrics with support vector machines to attenuate noise,” CSI Trans. ICT, vol. 2, no. 2, pp. 129–140, 2014.

[38]J. Muncaster and M. Turk, “Continuous Multimodal Authentication Using Dynamic Bayesian Networks,” Proc. Second Work. Multimodal User Authentication, 2006.

[39]M. C. Da Costa Abreu and M. Fairhurst, “Enhancing identity prediction using a novel approach to combining hard- and soft-biometric information,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 41, no. 5, pp. 599–607, 2011.

[40]A. K. Jain and U. Park, “Facial Marks: soft biometrics for face recognition,” 2009 16th IEEE Iternational Conf. Image Process., pp. 37–40, 2009.

[41]H. Lin, H. Lu, and L. Zhang, “A new automatic recognition system of gender, age and ethnicity,” Proc. World Congr. Intell. Control Autom., vol. 2, no. October, pp. 9988–9991, 2006.

[42]X. Lu and A. K. Jain, “Ethnicity Identification from Face Image,” Biometric Technol. Hum. Identif., vol. 5404, pp. 114–123, 2004.

[43]O. F. W. Onifade and K. T. Bamigbade, “GEHE: A multifactored model of soft and hard biometric trait for ease of retrieval,” 2014 World Congr. Comput. Appl. Inf. Syst. WCCAIS 2014, 2014.