Occluded Ear Recognition Using Block-Based PCA

  • V. Ratna Kumari
  • P. Rajesh KumarEmail author
  • S. Srinivasa Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)


In the real world, two non-intrusive biometric methods, namely face recognition and ear recognition, are interesting as they have flexibility in scanning the subject being tested. In particular, ear recognition is quite interesting as the ear pattern is stable in all emotions. This paper presents a novel way using block-based PCA to recognize ear even in case of partial occlusion. The other significance of the proposed method is that it can decide whether an input image is occluded or not. Conducted experiments, used a standard data set and shown that min–min fusion technique with city block distance metric is the apt ear recognition method when block-based PCA is used. The recognition rate with the proposed method is greater than 94%, and the equal error rate (EER) is less than 15% for a 25% occluded ear image.


Ear recognition Principal component analysis Euclidean distance City block distance Recognition rate Equal error rate 



This work is supported by University Grants Commission of India under the scheme—Minor Research Project No. MRP6023 Dated: 31. 10. 2016.


  1. 1.
    Abaza, A., Ross, A., Herbert, C., Harrison, M.A.F., Nixon, M.S.: A survey on ear biometrics. ACM Compu. Surv. 45(2) (2013)CrossRefGoogle Scholar
  2. 2.
    Emersic, Z., Struc, V., Peer, P.: Ear recognition: more than a survey. Neurocomputing 255, 26–39 (2017)CrossRefGoogle Scholar
  3. 3.
    Iannarelli, A.: Ear Identification, Forensic Identification Series. Paramount Publishing Company, Fremont, CA (1989)Google Scholar
  4. 4.
    Nejati, H., Zhang, L., Sim, T., Martinez-Marroquin, E., Dong, G.: Wonder ears: identification of identical twins from ear images. In: 21st International Conference on Pattern Recognition, pp. 1201–1204 (2012)Google Scholar
  5. 5.
    Chang, K., Bowyer, K.W., Sarkar, S., Victor, B.: Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1160–1165 (2003)CrossRefGoogle Scholar
  6. 6.
    Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Maui, Hawaii, USA, pp. 586–591 (1991)Google Scholar
  7. 7.
    Fan, Z., Ni, M., Sheng, M., Wu, Z., Xu, B.: Principal component analysis integrating Mahalanobis distance for face recognition. In: Second International Conference on Robot, Vision and Signal Processing, pp. 89–92 (2013)Google Scholar
  8. 8.
    Cha, S.-H.: Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Math. Models Methods Appl. Sci. 1(4), 300–307 (2007)Google Scholar
  9. 9.
    Golub, G.H., van Loan, C.F.: Matrix Computations, 3rd edn. John Hopkins University Press, Baltimore (1996)zbMATHGoogle Scholar
  10. 10.
    Liu, L., Wang, Y., Wang, Q., Tan, T.: Fast principal component analysis using eigen space merging. IEEE Int. Conf. Image Process. (ICIP) 6, 457–460 (2007)Google Scholar
  11. 11.
    Tharwat, A., Ibrahim, A., Ali, H.A.: Personal identification using ear images based on fast and accurate principal component analysis. In: INFOS2012, pp. 56–59, May 2012Google Scholar
  12. 12.
    Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space, pp. 420–434 (2001)Google Scholar
  13. 13.
    Querencias-uceta, D., Carmen, S.: Principal component analysis for ear-based biometric verification. IEEE conference, pp. 1–6 (2017)Google Scholar
  14. 14.
    Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. J. Pattern Recogn. 38(12), 2270–2285 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • V. Ratna Kumari
    • 1
  • P. Rajesh Kumar
    • 1
    Email author
  • S. Srinivasa Kumar
    • 2
  1. 1.Department of ECEPrsad V. Potluri Siddhartha Institute of TechnologyKanuruIndia
  2. 2.Department of ECEJNT UniversityKakinadaIndia

Personalised recommendations