Symmetric ear and profile face fusion for identical twins and non-twins recognition
- 169 Downloads
Abstract
Humans have bilateral body symmetry such that the left and right sides are mirror images of each other. This study tries to measure the performance on human recognition where the stored templates in the database are acquired from one side of a biometric trait such as left profile face, while the tested samples correspond to the other side of the same trait after applying a horizontal flip. Two different biometric traits are used in this study, namely profile face and ear biometrics. The experiments are conducted using the feature extraction methods namely Principal Component Analysis, Scale-Invariant Feature Transform, Local Binary Patterns, Local Phase Quantization and Binarized Statistical Image Features. Several experiments are performed on identical twins and non-twins individuals using ND-Twins-2009-2010 and UBEAR databases. Furthermore, the symmetry of profile face and ear is used to propose a hybrid approach of human recognition system that involves feature-level and score-level fusion of both traits. The proposed method is superior to all the unimodal and multimodal biometric methods that are implemented in this study for human recognition in the case of symmetry.
Keywords
Biometrics Symmetry Profile face Ear Identical twinsReferences
- 1.Arif, A., Li, T., Cheng, C.: Blurred fingerprint image enhancement: algorithm analysis and performance evaluation. Signal Image Video Process. 1–8 (2016). https://doi.org/10.1007/s11760-017-1218-0
- 2.Dubey, P., Kanumuri, T., Vyas, R.: Sequency codes for palmprint recognition. Signal Image Video Process. 1–8 (2017). https://doi.org/10.1007/s11760-017-1207-3
- 3.Alqaralleh, E., Toygar, Ö.: Ear recognition based on fusion of ear and tragus under different challenges. Int. J. Pattern Recognit. Artif. Intell. (2018). https://doi.org/10.1142/S0218001418560098
- 4.Gutta, S., Wechsler, H.: Face recognition using asymmetric faces. In: Zhang D., Jain A.K. (eds) Biometric Authentication. Lecture Notes in Computer Science, vol 3072. Springer, Berlin (2004)Google Scholar
- 5.Jin, W., Gong, F., Zeng, X., Fu, R.: Illumination robust face recognition using random projection and sparse representation. Signal Image Video Process. 1–9 (2017). https://doi.org/10.1007/s11760-017-1213-5
- 6.Oulefki, A., Mustapha, A., Boutellaa, E., Bengherabi, M., Tifarine, A.: Fuzzy reasoning model to improve face illumination invariance. Signal Image Video Process. 12, 421–428 (2017)CrossRefGoogle Scholar
- 7.Emeršič, Ž., Štruc, V., Peer, P.: Ear recognition: more than a survey. Neurocomputing 255, 26–39 (2017)CrossRefGoogle Scholar
- 8.Xiaoxun, Z., Yunde, J.: Symmetrical null space LDA for face and ear recognition. Neurocomputing 70(4), 842–848 (2007)CrossRefGoogle Scholar
- 9.Abaza, A., Ross, A.: Towards understanding the symmetry of human ears: a biometric perspective. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS). pp. 1–7 (2010)Google Scholar
- 10.Yan, P., Bowyer, K.: Empirical evaluation of advanced ear biometrics. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. pp. 41–41 (2005)Google Scholar
- 11.Passalis, G., Perakis, P., Theoharis, T., Kakadiaris, I.: Using facial symmetry to handle pose variations in real-world 3D face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1938–1951 (2011)CrossRefGoogle Scholar
- 12.Kirby, M., Sirovich, L.: Application of the Karhunen–Loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)CrossRefGoogle Scholar
- 13.Yang, Q., Ding, X.: Symmetrical PCA in face recognition. In: 2002 International Conference on Image Processing. 2002. Proceedings. pp. 97–100 (2002)Google Scholar
- 14.Liu, Y., Weaver, R., Schmidt, K., Serban, N., Cohn, J.: Facial asymmetry: a new biometric. Technical Report, CMU-RI-TR-01-23, The Robotics Institute of Carnegie Melon University, (2001)Google Scholar
- 15.Nejati, H., Zhang, L., Sim, T., Martinez-Marroquin, E., Dong, G.: Wonder ears: identification of identical twins from ear images. 2012 21st International Conference on Pattern Recognition (ICPR). pp. 1201–1204 (2012)Google Scholar
- 16.Afaneh, A., Noroozi, F., Toygar, Ö.: Recognition of identical twins using fusion of various facial feature extractors. EURASIP J. Image Video Process. 2017(81), 1–14 (2017)Google Scholar
- 17.Hadid, A., Ylioinas, J., Bengherabi, M., Ghahramani, M., Taleb-Ahmed, A.: Gender and texture classification: a comparative analysis using 13 variants of local binary patterns. Pattern Recognit. Lett. 68, 231–238 (2015)CrossRefGoogle Scholar
- 18.Farmanbar, M., Toygar, Ö.: A hybrid approach for person identification using palmprint and face biometrics. Int. J. Pattern Recognit. Artif. Intell. 29(06), 1556009-1–1556009-15 (2015)CrossRefGoogle Scholar
- 19.Jain, A., Ross, A.A., Nandakumar, K.: Introduction to biometrics. Springer, New York (2011)CrossRefGoogle Scholar
- 20.Kalyoncu, C., Toygar, Ö.: GTCLC: leaf classification method using multiple descriptors. IET Comput. Vis. 10(7), 700–708 (2016)CrossRefGoogle Scholar
- 21.Eskandari, M., Toygar, Ö.: Selection of optimized features and weights on face-iris fusion using distance images. Comput. Vis. Image Underst. 137, 63–75 (2015)CrossRefGoogle Scholar
- 22.Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
- 23.Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, New York (2010)zbMATHGoogle Scholar
- 24.Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
- 25.Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefzbMATHGoogle Scholar
- 26.Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. International Conference on Image and Signal Processing. pp. 236–243 (2008)Google Scholar
- 27.Pflug, A., Paul, P., Busch, C.: A comparative study on texture and surface descriptors for ear biometrics. In: 2014 International Carnahan Conference on Security Technology (ICCST). pp. 1–6 (2014)Google Scholar
- 28.Kannala, J., Rahtu, E.: Bsif: Binarized statistical image features. In: 2012 21st International Conference on Pattern Recognition (ICPR). pp. 1363–1366 (2012)Google Scholar
- 29.Farmanbar, M., Toygar, Ö.: Spoof detection on face and palmprint biometrics. Signal Image Video Process. 11, 1253–1260 (2017)CrossRefGoogle Scholar
- 30.Farmanbar, M., Toygar, Ö.: Feature selection for the fusion of face and palmprint biometrics. Signal Image Video Process. 10(5), 951–958 (2016)CrossRefGoogle Scholar
- 31.Eskandari, M., Toygar, Ö.: Fusion of face and iris biometrics using local and global feature extraction methods. Signal Image Video Process. 8(6), 995–1006 (2014)CrossRefGoogle Scholar
- 32.Phillips, P.J., Flynn, P.J., Bowyer, K.W., Bruegge, R.W.V., Grother, P.J., Quinn, G.W., Pruitt, M.: Distinguishing identical twins by face recognition. In: Automatic Face & Gesture Recognition and Workshops. pp. 185–192 (2011)Google Scholar
- 33.CVRL Data Sets (2013) [Online] https://sites.google.com/a/nd.edu/public-cvrl/data-sets
- 34.Raposo, R., Hoyle, E., Peixinho, A., Proença, H.: Computational Intelligence in Biometrics and Identity Management (CIBIM). In: 2011 IEEE Workshop on pp. 84–90 (2011)Google Scholar
- 35.Nanni, L., Lumini, A., Brahnam, S.: Survey on LBP based texture descriptors for image classification. Expert Syst Appl. 39(3), 3634–3641 (2012)CrossRefGoogle Scholar