Classification of Ear Images According to Person, Age, and Gender with The Local Ternary Pattern

Yıl 2022, , 1003 - 1022, 31.12.2022

Emrah Aydemir , Asaad Qais Shalal Abo Soot

https://doi.org/10.17482/uumfd.1056921

Öz

The need to verify the identity of individuals is increasing day by day. Traditionally, passports, identity cards, keys are used in authentication systems. With such systems, passwords can also be used to increase security. Unfortunately, the disadvantages of such security systems include the loss, copying, and theft of the item used as an identity. Passwords can be forgotten. Such situations can endanger the person or put him in a difficult situation. Such shortcomings of traditional person recognition techniques cause major problems for everyone. Such situations push researchers to seek a solid, reliable and perfect personal description. This search pushes researchers to biometric systems. In this study, 2000 data, which are right and left ear images of 100 people, were collected. The attributes of these collected files were extracted with the Local Triple Pattern. For each image file, 1x512 vectors were produced. These processes were performed for all files and images were classified for person, age and gender with many different classification algorithms. While 90.2% accuracy rate was obtained for person recognition, 99.8% success was achieved for gender. Finally, the classification success rate was 86.1% for age.

Anahtar Kelimeler

Ear image, local ternary pattern, classification

YEREL ÜÇLÜ DESEN İLE KULAK GÖRÜNTÜLERİNİN KİŞİ, YAŞ VE CİNSİYETE GÖRE SINIFLANDIRILMASI

Öz

Bireylerin kimliğini doğrulamaya yönelik ihtiyaç her geçen gün artmaktadır. Geleneksel olarak kimlik doğrulama sistemlerinde pasaportlar, kimlik kartları, anahtarlar kullanılır. Bu tür sistemler ile birlikte güvenliği arttırmak için şifreler de kullanılabilir. Maalesef bu tür güvenlik sistemlerinin dezavantajları arasında kimlik olarak kullanılan eşyanın kaybolması, kopyalanması, çalınması söz konusu olabilir. Şifrelerin ise unutulması ortaya çıkabilir. Bu tür durumlar kişiyi tehlikeye atabilir veya zor bir duruma sokabilir. Geleneksel kişi tanıma tekniklerinin bu tür eksiklikleri, herkes için büyük sorunlara neden olur. Bu tür durumlar ise araştırmacıları sağlam, güvenilir ve kusursuz bir kişisel tanımlama arayışına itmektedir. Bu arayış ise araştırmacıları biyometri sistemlerine itmektedir. Buradaki çalışma da 100 kişiye ait sağ ve sol kulak görüntüleri olan 2000 veri toplanmıştır. Toplanan bu dosyaların Yerel Üçlü Desen ile öznitelikleri çıkarılmıştır. Her bir görüntü dosyası için 1x512 boyutlarında vektör üretilmiştir. Tüm dosyalar için bu işlemler yapılmış ve birçok farklı sınıflandırma algoritmaları ile görüntüler kişi, yaş ve cinsiyet için sınıflandırılmıştır. Kişi tanıma için % 90,2 oranında doğruluk oranı elde edilirken, cinsiyet için % 99,8 oranında başarı elde edilmiştir. Son olarak yaş için ise % 86,1 oranında sınıflandırma başarısına ulaşılmıştır.

Anahtar Kelimeler

kulak görüntüsü, yerel üçlü desen, sınıflandırma

Kaynakça

• 1. Abaza, A., Hebert, C., Harrison, M. A. F. (2010). Fast learning ear detection for real-time surveillance. 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS). doi:10.1109/BTAS.2010.5634486
• 2. Agarwal, M., Singhal, A., Lall, B. J. P. A., Applications. (2019). Multi-channel local ternary pattern for content-based image retrieval. 22(4), 1585-1596. doi:10.1007/s10044-019-00787-2
• 3. Ahmed, A. A. and Omer, N. (2015). Estimation of sex from the anthropometric ear measurements of a Sudanese population. Legal Medicine, 17(5), 313-319. doi:10.1016/j.legalmed.2015.03.002
• 4. Aishna Sharma, N. L., Mani Roja M. Edinburgh. (2019). Biometric Identification using Human Ear. International Journal of Engineering and Advanced Technology (IJEAT), 9. doi:10.35940/ijeat.A2027.109119
• 5. Alkababji, A. M. and Mohammed, O. H. (2021). Real time ear recognition using deep learning. Telkomnika, 19(2), 523-530. doi:10.12928/telkomnika.v19i2.18322
• 6. Alshazly, H., Linse, C., Barth, E., Idris, S. A., Martinetz, T. (2021). Towards Explainable Ear Recognition Systems Using Deep Residual Networks. IEEE Access. doi:10.1109/ACCESS.2021.3109441
• 7. Alshazly, H., Linse, C., Barth, E., Martinetz, T. (2019). Handcrafted versus CNN features for ear recognition. Symmetry, 11(12), 1493. doi:10.3390/sym11121493
• 8. Ban, K. and Jung, E. S. (2020). Ear shape categorization for ergonomic product design. International Journal of Industrial Ergonomics, 102962. doi:10.1016/j.ergon.2020.102962
• 9. Benzaoui, A., Adjabi, I., Boukrouche, A. (2016). Person identification based on ear morphology. 2016 International Conference on Advanced Aspects of Software Engineering (ICAASE). doi:10.1109/ICAASE.2016.7843851
• 10. Benzaoui, A., Kheider, A., Boukrouche, A. (2015). Ear description and recognition using ELBP and wavelets. 2015 International Conference on Applied Research In Computer Science And Engineering (Icar). doi:10.1109/ARCSE.2015.7338146
• 11. Bertillon, A. (1890). La photographie judiciaire: avec un appendice sur la classification et l'identification anthropométriques. Paris: Gauthier-Villars.
• 12. Broer, P. N., Thiha, A., Ehrl, D., Sinno, S., Juran, S., Szpalski, C., Ng, R., Ninkovic, M., Prantl, L., Heidekrueger, P. I. (2018). The ideal ear position in Caucasian females. Journal of Cranio-Maxillofacial Surgery, 46(3), 485-491. doi:10.1016/j.jcms.2017.12.017
• 13. Burge, M. and Burger, W. (2000). Ear biometrics in computer vision. Proceedings 15th International Conference on Pattern Recognition. ICPR-2000. doi:10.1109/ICPR.2000.906202
• 14. Chang, K., Bowyer, K. W., Sarkar, S., Victor, B. (2003). Comparison and combination of ear and face images in appearance-based biometrics. IEEE transactions on pattern analysis and machine intelligence, 25(9), 1160-1165. doi:10.1109/TPAMI.2003.1227990
• 15. Chen, L., Mu, Z., Zhang, B., Zhang, Y. (2015). Ear recognition from one sample per person. PloS one, 10(5), e0129505. doi:10.1371/journal.pone.0129505
• 16. Choraś, M. (2008). Perspective methods of human identification: ear biometrics. Opto-electronics review, 16(1), 85-96. doi:10.2478/s11772-007-0033-5
• 17. Fahmi, P. A., Kodirov, E., Choi, D.-J., Lee, G.-S., Azli, A. M. F., Sayeed, S. (2012). Implicit authentication based on ear shape biometrics using smartphone camera during a call. 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC). doi:10.1109/ICSMC.2012.6378079
• 18. Farkas, L. G., Posnick, J. C., Hreczko, T. M. (1992). Anthropometric growth study of the head. The Cleft Palate-Craniofacial Journal, 29(4), 303-308. doi:10.1597/1545-1569_1992_029_0303_agsoth_2.3.co_2
• 19. Hassaballah, M., Alshazly, H. A., Ali, A. A. (2019). Ear recognition using local binary patterns: A comparative experimental study. Expert Systems with Applications, 118, 182-200. doi:10.1016/j.eswa.2018.10.007
• 20. Attalla, S. M., Kumar, K. A., Hussain, N. (2020). Study of the Ear Shape and the Lobule Attachement among the Adult Malaysian Population at Shah Alam. European Journal of Molecular & Clinical Medicine, 7(3), 5417-5425.
• 21. Iannarelli, A. V. (1964). Ear identification. Paramont Publishing Company.
• 22. Jain, A. K., Ross, A., Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4-20. doi:10.1109/TCSVT.2003.818349
• 23. Jiddah, S. M. and Yurtkan, K. (2018). Fusion of geometric and texture features for ear recognition. 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). doi:10.1109/ISMSIT.2018.8567044
• 24. Jung, H. S. and Jung, H. S. (2003). Surveying the dimensions and characteristics of Korean ears for the ergonomic design of ear-related products. International journal of industrial ergonomics, 31(6), 361-373. doi:10.1016/S0169-8141(02)00237-8
• 25. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai,
• 26. Krishan, K., Kanchan, T., Thakur, S. (2019). A study of morphological variations of the human ear for its applications in personal identification. Egyptian Journal of Forensic Sciences, 9(1), 1-11. doi:10.1186/s41935-019-0111-0
• 27. Kumar, A. and Wu, C. (2012). Automated human identification using ear imaging. Pattern Recognition, 45(3), 956-968. doi:10.1016/j.patcog.2011.06.005
• 28. Lannarelli, A. (1989). Ear Identification. Paramount Publishing Company.
• 29. Larson, S. C. (1931). The shrinkage of the coefficient of multiple correlation. Journal of Educational Psychology, 22(1), 45. doi:10.1037/h0072400
• 30. Lee, W., Yang, X., Jung, H., Bok, I., Kim, C., Kwon, O., You, H. (2018). Anthropometric analysis of 3D ear scans of Koreans and Caucasians for ear product design. Ergonomics, 61(11), 1480-1495. doi:10.1080/00140139.2018.1493150
• 31. Lu, L., Zhang, X., Zhao, Y., Jia, Y. (2006). Ear recognition based on statistical shape model. First International Conference on Innovative Computing, Information and Control-Volume I (ICICIC'06). doi:10.1109/ICICIC.2006.445
• 32. Mangayarkarasi, N., Raghuraman, G., Nasreen, A. J. P. C. S. (2019). Contour Detection based Ear Recognition for Biometric Applications. 165, 751-758. doi:10.1016/j.procs.2020.01.016
• 33. Mayya, A. M. and Saii, M. M. (2016). Human recognition based on ear shape images using PCA-Wavelets and different classification methods. Medical Devices and Diagnostic Engineering, 10, 11-18. doi:10.15761/MDDE.1000103
• 34. Moreno, B., Sanchez, A., Vélez, J. F. (1999). On the use of outer ear images for personal identification in security applications. Proceedings IEEE 33rd Annual 1999 International Carnahan Conference on Security Technology (Cat. No. 99CH36303). doi:10.1109/CCST.1999.797956
• 35. Mosteller, F. and Wallace, D. L. (1963). Inference in an authorship problem: A comparative study of discrimination methods applied to the authorship of the disputed Federalist Papers. Journal of the American Statistical Association, 58(302), 275-309. doi:10.1080/01621459.1963.10500849
• 36. Naseem, I., Togneri, R., Bennamoun, M. (2008). Sparse representation for ear biometrics. International Symposium on Visual Computing. doi:10.1007/978-3-540-89646-3_33
• 37. Omara, I., Li, F., Zhang, H., Zuo, W. (2016). A novel geometric feature extraction method for ear recognition. Expert Systems with Applications, 65, 127-135. doi:10.1016/j.eswa.2016.08.035
• 38. Othman, R. N., Alizadeh, F., Sutherland, A. (2018). A novel approach for occluded ear recognition based on shape context. 2018 International Conference on Advanced Science and Engineering (ICOASE). doi:10.1109/ICOASE.2018.8548856
• 39. Priyadharshini, R. A., Arivazhagan, S., Arun, M. J. A. I. (2020). A deep learning approach for person identification using ear biometrics. 1-12. doi:10.1007/s10489-020-01995-8
• 40. Rahman, M., Islam, M. R., Bhuiyan, N. I., Ahmed, B., Islam, A. (2007). Person identification using ear biometrics. International Journal of The Computer, the Internet and Management, 15(2), 1-8. doi:10.4038/sljp.v8i0.208
• 41. Rakshit, R. D., Nath, S. C., Kisku, D. R. (2018). Face identification using some novel local descriptors under the influence of facial complexities. Expert Systems with Applications, 92, 82-94. doi:10.1016/j.eswa.2017.09.038
• 42. Rani, D., Krishan, K., Sahani, R., Baryah, N., Kanchan, T. (2020). Evaluation of Morphological Characteristics of the Human Ear in Young Adults. Journal of Craniofacial Surgery, 31(6), 1692-1698. doi:10.1097/SCS.0000000000006394
• 43. Refaeilzadeh, P., Tang, L., Liu, H. (2009). Cross-Validation. In L. Liu & M. T. Özsu (Eds.), Encyclopedia of Database Systems (pp. 532-538). Springer US. doi:10.1007/978-0-387-39940-9_565
• 44. Ross, A. and Abaza, A. (2011). Human ear recognition. Computer, 44(11), 79-81. doi:10.1109/MC.2011.344
• 45. Said, E. H., Abaza, A., Ammar, H. (2008). Ear segmentation in color facial images using mathematical morphology. 2008 Biometrics Symposium. doi:10.1109/BSYM.2008.4655519
• 46. Sajadi, S. and Fathi, A. (2020). Genetic algorithm based local and global spectral features extraction for ear recognition. Expert Systems with Applications, 159, 113639. doi:10.1016/j.eswa.2020.113639
• 47. Sibai, F. N., Nuaimi, A., Maamari, A., Kuwair, R. (2013). Ear recognition with feed-forward artificial neural networks. Neural Computing and Applications, 23(5), 1265-1273. doi:10.1007/s00521-012-1068-1
• 48. Tan, X. and Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE transactions on image processing, 19(6), 1635-1650. doi:10.1109/TIP.2010.2042645
• 49. Tariq, A. and Akram, M. U. J. T. T. C. E. C. (2012). Personal identification using ear recognition. 10(2), 321-326. doi:10.12928/telkomnika.v10i2.801
• 50. Uddin, M. N., Sharmin, S., Ahmed, A., Hasan, E. (2011). A survey of biometrics security system. IJCSNS, 11(10), 16.
• 51. Yaman, D., Eyiokur, F. I., Sezgin, N., Ekenel, H. K. (2018). Age and gender classification from ear images. 2018 International Workshop on Biometrics and Forensics (IWBF). doi:10.1109/IWBF.2018.8401568
• 52. Yan, P., Bowyer, K. (2005). Empirical evaluation of advanced ear biometrics. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)-Workshops. doi:10.1109/CVPR.2005.450
• 53. Ying, T., Shining, W., Wanxiang, L. (2018). Human ear recognition based on deep convolutional neural network. 2018 Chinese Control And Decision Conference (CCDC). doi:10.1109/CCDC.2018.8407424
• 54. Yoon, H.-Y. and Jung, S.-G. (2002). A study of measurement on the head and face for Korean adults. IE interfaces, 15(2), 199-208.
• 55. Yuan, L. and Mu, Z. (2014). Ear recognition based on Gabor features and KFDA. The Scientific World Journal, 2014. doi:10.1155/2014/702076