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IMAGE FUSION AND DEEP LEARNING BASED EAR RECOGNITION USING THERMAL AND VISIBLE IMAGES

Yıl 2023, , 997 - 1009, 03.12.2023

Mücahit Cihan , Murat Ceylan

https://doi.org/10.17780/ksujes.1345020

Öz

Advances in imaging and deep learning have fueled interest in ear biometrics, as the structure of the ear offers unique identification features. Thermal and visible ear images capture different aspects of these features. Thermal images are light-independent, and visible images excel at capturing texture details. Combining these images creates more feature-rich composite images. This study examines the fusion of thermal and visible ear images taken under varying lighting conditions to enhance automatic ear recognition. The image fusion process involved three distinct multiresolution analysis methods: discrete wavelet transform, ridgelet transform, and curvelet transform. Subsequently, a specially designed deep learning model was used for ear recognition. The results of this study reveal that employing the complex-valued curvelet transform and thermal images achieved an impressive recognition rate of 96.82%, surpassing all other methods. Conversely, visible images exhibited the lowest recognition rate of 75.00%, especially in low-light conditions. In conclusion, the fusion of multiple data sources significantly enhances ear recognition effectiveness, and the proposed model consistently achieves remarkable recognition rates even when working with a limited number of fused ear images.

Anahtar Kelimeler

Ear recognition image fusion deep learning multi-resolution analysis methods thermal and visible images

Kaynakça

  • Abaza, A., & Bourlai, T. (2012, May). Human ear detection in the thermal infrared spectrum. In Thermosense: Thermal Infrared Applications XXXIV, 8354, 286-295. https://doi.org/10.1117/12.919285
  • Abd Almisreb, A., Jamil, N., & Din, N. M. (2018, March). Utilizing AlexNet deep transfer learning for ear recognition. In 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), 1-5. DOI: 10.1109/INFRKM.2018.8464769
  • Alshazly, H., Linse, C., Barth, E., & Martinetz, T. (2019). Handcrafted versus CNN features for ear recognition. Symmetry, 11(12), 1493. https://doi.org/10.3390/sym11121493
  • AlZubi, S., Sharif, M. S., Islam, N., & Abbod, M. (2011, May). Multi-resolution analysis using curvelet and wavelet transforms for medical imaging. In 2011 IEEE international symposium on medical measurements and applications, 188-191. DOI: 10.1109/MeMeA.2011.5966687
  • Ariffin, S. M. Z. S. Z., Jamil, N., & Rahman, P. N. M. A. (2016, September). DIAST variability illuminated thermal and visible ear images datasets. In 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 191-195. DOI: 10.1109/SPA.2016.7763611
  • Ariffin, S. M. Z. S. Z., Jamil, N., & Rahman, P. N. M. A. (2017, May). Can thermal and visible image fusion improves ear recognition?. In 2017 8th International Conference on Information Technology (ICIT), 780-784. DOI: 10.1109/ICITECH.2017.8079945
  • Ashiq, F., Asif, M., Ahmad, M. B., Zafar, S., Masood, K., Mahmood, T., Mahmood, M. T., & Lee, I. H. (2022). CNN-based object recognition and tracking system to assist visually impaired people. IEEE Access, 10, 14819-14834. DOI: 10.1109/ACCESS.2022.3148036
  • Benzaoui, A., Kheider, A., & Boukrouche, A. (2015, October). Ear description and recognition using ELBP and wavelets. In 2015 International Conference on Applied Research In Computer Science And Engineering (Icar), 1-6. DOI: 10.1109/ARCSE.2015.7338146
  • Bertillon, A., & McClaughry, R. W. (1896). Signaletic instructions including the theory and practice of anthropometrical identification. Werner Company.
  • Candès, E. J., & Donoho, D. L. (1999). Ridgelets: A key to higher-dimensional intermittency?. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 357(1760), 2495-2509. https://doi.org/10.1098/rsta.1999.0444
  • Candes, E., Demanet, L., Donoho, D., & Ying, L. (2006). Fast discrete curvelet transforms. Multiscale Modeling & Simulation, 5(3), 861-899. https://doi.org/10.1137/05064182
  • Chen, D., Tang, J., Xi, H., & Zhao, X. (2021). Image Recognition of Modern Agricultural Fruit Maturity Based on Internet of Things. Traitement du Signal, 38(4). DOI: 10.18280/ts.380435
  • Choi, J., Hu, S., Young, S. S., & Davis, L. S. (2012, May). Thermal to visible face recognition. In Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring II; and Biometric Technology for Human Identification IX, 8371, 252-261. https://doi.org/10.1117/12.920330
  • Cihan, M., & Ceylan, M. (2021). Fusion of CT and MR Liver Images Using Multiresolution Analysis Methods. Avrupa Bilim ve Teknoloji Dergisi, (30), 56-61. https://doi.org/10.31590/ejosat.1005858
  • Cihan, M., Ceylan, M., & Ornek, A. H. (2022a). Spectral-spatial classification for non-invasive health status detection of neonates using hyperspectral imaging and deep convolutional neural networks. Spectroscopy Letters, 1-14. https://doi.org/10.1080/00387010.2022.2076698
  • Cihan, M., Ceylan, M., Soylu, H., & Konak, M. (2022b). Fast Evaluation of Unhealthy and Healthy Neonates Using Hyperspectral Features on 700-850 Nm Wavelengths, ROI Extraction, and 3D-CNN. IRBM, 43(5), 362-371. https://doi.org/10.1016/j.irbm.2021.06.009
  • Ciresan, D. C., Meier, U., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2011, June). Flexible, high performance convolutional neural networks for image classification. In Twenty-second international joint conference on artificial intelligence. DOI: 10.5591/978-1-57735-516-8/IJCAI11-210
  • Do, M. N., & Vetterli, M. (2003). The finite ridgelet transform for image representation. IEEE Transactions on image Processing, 12(1), 16-28. DOI: 10.1109/TIP.2002.806252
  • El-Naggar, S., & Bourlai, T. (2022). Exploring Deep Learning Ear Recognition in Thermal Images. IEEE Transactions on Biometrics, Behavior, and Identity Science, 5(1), 64-75. DOI: 10.1109/TBIOM.2022.3218151
  • Emeršič, Ž., Štepec, D., Štruc, V., & Peer, P. (2017). Training convolutional neural networks with limited training data for ear recognition in the wild. arXiv preprint arXiv:1711.09952. https://doi.org/10.48550/arXiv.1711.09952
  • Emeršič, Ž., Štruc, V., & Peer, P. (2017). Ear recognition: More than a survey. Neurocomputing, 255, 26-39. https://doi.org/10.1016/j.neucom.2016.08.139
  • Fadili, J. M., & Starck, J. L. (2009). Curvelets and ridgelets. https://doi.org/10.1007/978-0-387-30440-3_111
  • Fields, C., Falls, H. C., Warren, C. P., & Zimberoff, M. (1960). The ear of the newborn as an identification constant. Obstetrics & Gynecology, 16(1), 98-102.
  • Galdámez, P. L., Raveane, W., & Arrieta, A. G. (2017). A brief review of the ear recognition process using deep neural networks. Journal of Applied Logic, 24, 62-70. https://doi.org/10.1016/j.jal.2016.11.014
  • Guérin, J., Thiery, S., Nyiri, E., Gibaru, O., & Boots, B. (2021). Combining pretrained CNN feature extractors to enhance clustering of complex natural images. Neurocomputing, 423, 551-571. https://doi.org/10.1016/j.neucom.2020.10.068
  • Gutiérrez, L., Melin, P., & Lopez, M. (2010, July). Modular neural network integrator for human recognition from ear images. In The 2010 International Joint Conference on Neural Networks (IJCNN), 1-5. DOI: 10.1109/IJCNN.2010.5596633
  • Haghighat, M. B. A., Aghagolzadeh, A., & Seyedarabi, H. (2011). Multi-focus image fusion for visual sensor networks in DCT domain. Computers & Electrical Engineering, 37(5), 789-797. https://doi.org/10.1016/j.compeleceng.2011.04.016
  • Jain, A. K., Flynn, P., & Ross, A. A. (Eds.). (2007). Handbook of biometrics. Springer Science & Business Media.
  • Jamil, N., AlMisreb, A., & Halin, A. A. (2014). Illumination-invariant ear authentication. Procedia Computer Science, 42, 271-278. https://doi.org/10.1016/j.procs.2014.11.062
  • Kong, S. G., Heo, J., Boughorbel, F., Zheng, Y., Abidi, B. R., Koschan, A., Yi, M., & Abidi, M. A. (2007). Multiscale fusion of visible and thermal IR images for illumination-invariant face recognition. International Journal of Computer Vision, 71(2), 215-233. https://doi.org/10.1007/s11263-006-6655-0
  • Lannarelli, A. (1989). Ear identification. Forensic identification series.
  • Ma, Y., Huang, Z., Wang, X., & Huang, K. (2020). An overview of multimodal biometrics using the face and ear. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/6802905
  • Maity, S., Abdel-Mottaleb, M., & Asfour, S. S. (2020). Multimodal biometrics recognition from facial video with missing modalities using deep learning. Journal of Information Processing Systems, 16(1), 6-29. DOI: 10.3745/JIPS.02.0129
  • Moreno, B., Sanchez, A., & Vélez, J. F. (1999, October). On the use of outer ear images for personal identification in security applications. In Proceedings IEEE 33rd Annual 1999 International Carnahan Conference on Security Technology (Cat. No. 99CH36303), 469-476. DOI: 10.1109/CCST.1999.797956
  • Morlet, J., Arens, G., Fourgeau, E., & Glard, D. (1982). Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media. Geophysics, 47(2), 203-221. https://doi.org/10.1190/1.1441328
  • Nejati, H., Zhang, L., Sim, T., Martinez-Marroquin, E., & Dong, G. (2012, November). Wonder ears: Identification of identical twins from ear images. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 1201-1204.
  • Pajares, G., & De La Cruz, J. M. (2004). A wavelet-based image fusion tutorial. Pattern recognition, 37(9), 1855-1872. https://doi.org/10.1016/j.patcog.2004.03.010
  • Pflug, A., Paul, P. N., & Busch, C. (2014, October). A comparative study on texture and surface descriptors for ear biometrics. In 2014 International carnahan conference on security technology (ICCST), 1-6. DOI: 10.1109/CCST.2014.6986993
  • Rane, M. E., & Bhadade, U. (2020, December). Face and palmprint Biometric recognition by using weighted score fusion technique. In 2020 IEEE Pune Section International Conference (PuneCon), 11-16. DOI: 10.1109/PuneCon50868.2020.9362433
  • Sarangi, P. P., Mishra, B. P., & Dehuri, S. (2018, March). Multimodal biometric recognition using human ear and profile face. In 2018 4th International Conference on Recent Advances in Information Technology (RAIT), 1-6. DOI: 10.1109/RAIT.2018.8389035
  • Sarangi, P. P., Nayak, D. R., Panda, M., & Majhi, B. (2022). A feature-level fusion based improved multimodal biometric recognition system using ear and profile face. Journal of Ambient Intelligence and Humanized Computing, 13(4), 1867-1898. https://doi.org/10.1007/s12652-021-02952-0
  • Seal, A., Bhattacharjee, D., Nasipuri, M., Gonzalo-Martin, C., & Menasalvas, E. (2017). Fusion of visible and thermal images using a directed search method for face recognition. International Journal of Pattern Recognition and Artificial Intelligence, 31(04), 1756005. https://doi.org/10.1142/S0218001417560055
  • Singh, S., Gyaourova, A., Bebis, G., & Pavlidis, I. (2004, August). Infrared and visible image fusion for face recognition. In Biometric technology for human identification, 5404, 585-596. https://doi.org/10.1117/12.543549
  • Starck, J. L., Donoho, D. L., & Candès, E. J. (2003). Astronomical image representation by the curvelet transform. Astronomy & Astrophysics, 398(2), 785-800. DOI: 10.1051/0004-6361:20021571
  • Toygar, Ö., Alqaralleh, E., & Afaneh, A. (2018). Person identification using multimodal biometrics under different challenges. Human-Robot Interaction-Theory and Application, 81-96. DOI: 10.5772/intechopen.71667
  • Victor, B., Bowyer, K., & Sarkar, S. (2002, August). An evaluation of face and ear biometrics. In 2002 International Conference on Pattern Recognition, 1, 429-432. DOI: 10.1109/ICPR.2002.1044746
  • Yu, H., Yang, L. T., Zhang, Q., Armstrong, D., & Deen, M. J. (2021). Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing, 444, 92-110. https://doi.org/10.1016/j.neucom.2020.04.157

TERMAL VE GÖRÜNÜR GÖRÜNTÜLER KULLANILARAK GÖRÜNTÜ BİRLEŞTİRME VE DERİN ÖĞRENME TABANLI KULAK TANIMA

Yıl 2023, , 997 - 1009, 03.12.2023

Mücahit Cihan , Murat Ceylan

https://doi.org/10.17780/ksujes.1345020

Öz

Görüntüleme ve derin öğrenme alanındaki gelişmeler, kulağın yapısı benzersiz tanımlama özellikleri sunduğundan kulak biyometrisine olan ilgiyi artırmıştır. Termal ve görünür kulak görüntüleri bu özelliklerin farklı yönlerini yakalar. Termal görüntüler ışıktan bağımsızdır ve görünür görüntüler doku ayrıntılarını yakalamada mükemmeldir. Bu görüntülerin birleştirilmesi daha zengin özelliklere sahip kompozit görüntüler oluşturur. Bu çalışma, otomatik kulak tanımayı geliştirmek amacıyla farklı aydınlatma koşulları altında elde edilen termal ve görünür kulak görüntülerinin birleştirilmesini incelemektedir. Görüntü birleştirme işlemi üç farklı çok çözünürlüklü analiz yöntemini içermektedir: ayrık dalgacık dönüşümü, ridgelet dönüşümü ve curvelet dönüşümü. Ardından, kulak tanıma için özel olarak tasarlanmış derin öğrenme modeli kullanılmıştır. Bu çalışmanın sonuçları, karmaşık değerli curvelet dönüşümü ve termal görüntülerin kullanılmasının, diğer tüm yöntemleri geride bırakarak %96.82 gibi etkileyici bir tanıma oranı elde ettiğini ortaya koymaktadır. Buna karşılık, görünür görüntüler özellikle düşük ışık koşullarında %75.00 ile en düşük tanıma oranını sergilemiştir. Sonuç olarak, birden fazla veri kaynağının birleştirilmesi kulak tanıma etkinliğini önemli ölçüde artırmaktadır ve önerilen model, sınırlı sayıda birleştirilmiş kulak görüntüsüyle çalışırken bile tutarlı bir şekilde dikkate değer tanıma oranlarına ulaşmaktadır.

Anahtar Kelimeler

Kulak tanıma görüntü birleştirme derin öğrenme çoklu çözünürlüklü analiz yöntemleri termal ve görünür görüntüler

Kaynakça

  • Abaza, A., & Bourlai, T. (2012, May). Human ear detection in the thermal infrared spectrum. In Thermosense: Thermal Infrared Applications XXXIV, 8354, 286-295. https://doi.org/10.1117/12.919285
  • Abd Almisreb, A., Jamil, N., & Din, N. M. (2018, March). Utilizing AlexNet deep transfer learning for ear recognition. In 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), 1-5. DOI: 10.1109/INFRKM.2018.8464769
  • Alshazly, H., Linse, C., Barth, E., & Martinetz, T. (2019). Handcrafted versus CNN features for ear recognition. Symmetry, 11(12), 1493. https://doi.org/10.3390/sym11121493
  • AlZubi, S., Sharif, M. S., Islam, N., & Abbod, M. (2011, May). Multi-resolution analysis using curvelet and wavelet transforms for medical imaging. In 2011 IEEE international symposium on medical measurements and applications, 188-191. DOI: 10.1109/MeMeA.2011.5966687
  • Ariffin, S. M. Z. S. Z., Jamil, N., & Rahman, P. N. M. A. (2016, September). DIAST variability illuminated thermal and visible ear images datasets. In 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 191-195. DOI: 10.1109/SPA.2016.7763611
  • Ariffin, S. M. Z. S. Z., Jamil, N., & Rahman, P. N. M. A. (2017, May). Can thermal and visible image fusion improves ear recognition?. In 2017 8th International Conference on Information Technology (ICIT), 780-784. DOI: 10.1109/ICITECH.2017.8079945
  • Ashiq, F., Asif, M., Ahmad, M. B., Zafar, S., Masood, K., Mahmood, T., Mahmood, M. T., & Lee, I. H. (2022). CNN-based object recognition and tracking system to assist visually impaired people. IEEE Access, 10, 14819-14834. DOI: 10.1109/ACCESS.2022.3148036
  • Benzaoui, A., Kheider, A., & Boukrouche, A. (2015, October). Ear description and recognition using ELBP and wavelets. In 2015 International Conference on Applied Research In Computer Science And Engineering (Icar), 1-6. DOI: 10.1109/ARCSE.2015.7338146
  • Bertillon, A., & McClaughry, R. W. (1896). Signaletic instructions including the theory and practice of anthropometrical identification. Werner Company.
  • Candès, E. J., & Donoho, D. L. (1999). Ridgelets: A key to higher-dimensional intermittency?. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 357(1760), 2495-2509. https://doi.org/10.1098/rsta.1999.0444
  • Candes, E., Demanet, L., Donoho, D., & Ying, L. (2006). Fast discrete curvelet transforms. Multiscale Modeling & Simulation, 5(3), 861-899. https://doi.org/10.1137/05064182
  • Chen, D., Tang, J., Xi, H., & Zhao, X. (2021). Image Recognition of Modern Agricultural Fruit Maturity Based on Internet of Things. Traitement du Signal, 38(4). DOI: 10.18280/ts.380435
  • Choi, J., Hu, S., Young, S. S., & Davis, L. S. (2012, May). Thermal to visible face recognition. In Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring II; and Biometric Technology for Human Identification IX, 8371, 252-261. https://doi.org/10.1117/12.920330
  • Cihan, M., & Ceylan, M. (2021). Fusion of CT and MR Liver Images Using Multiresolution Analysis Methods. Avrupa Bilim ve Teknoloji Dergisi, (30), 56-61. https://doi.org/10.31590/ejosat.1005858
  • Cihan, M., Ceylan, M., & Ornek, A. H. (2022a). Spectral-spatial classification for non-invasive health status detection of neonates using hyperspectral imaging and deep convolutional neural networks. Spectroscopy Letters, 1-14. https://doi.org/10.1080/00387010.2022.2076698
  • Cihan, M., Ceylan, M., Soylu, H., & Konak, M. (2022b). Fast Evaluation of Unhealthy and Healthy Neonates Using Hyperspectral Features on 700-850 Nm Wavelengths, ROI Extraction, and 3D-CNN. IRBM, 43(5), 362-371. https://doi.org/10.1016/j.irbm.2021.06.009
  • Ciresan, D. C., Meier, U., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2011, June). Flexible, high performance convolutional neural networks for image classification. In Twenty-second international joint conference on artificial intelligence. DOI: 10.5591/978-1-57735-516-8/IJCAI11-210
  • Do, M. N., & Vetterli, M. (2003). The finite ridgelet transform for image representation. IEEE Transactions on image Processing, 12(1), 16-28. DOI: 10.1109/TIP.2002.806252
  • El-Naggar, S., & Bourlai, T. (2022). Exploring Deep Learning Ear Recognition in Thermal Images. IEEE Transactions on Biometrics, Behavior, and Identity Science, 5(1), 64-75. DOI: 10.1109/TBIOM.2022.3218151
  • Emeršič, Ž., Štepec, D., Štruc, V., & Peer, P. (2017). Training convolutional neural networks with limited training data for ear recognition in the wild. arXiv preprint arXiv:1711.09952. https://doi.org/10.48550/arXiv.1711.09952
  • Emeršič, Ž., Štruc, V., & Peer, P. (2017). Ear recognition: More than a survey. Neurocomputing, 255, 26-39. https://doi.org/10.1016/j.neucom.2016.08.139
  • Fadili, J. M., & Starck, J. L. (2009). Curvelets and ridgelets. https://doi.org/10.1007/978-0-387-30440-3_111
  • Fields, C., Falls, H. C., Warren, C. P., & Zimberoff, M. (1960). The ear of the newborn as an identification constant. Obstetrics & Gynecology, 16(1), 98-102.
  • Galdámez, P. L., Raveane, W., & Arrieta, A. G. (2017). A brief review of the ear recognition process using deep neural networks. Journal of Applied Logic, 24, 62-70. https://doi.org/10.1016/j.jal.2016.11.014
  • Guérin, J., Thiery, S., Nyiri, E., Gibaru, O., & Boots, B. (2021). Combining pretrained CNN feature extractors to enhance clustering of complex natural images. Neurocomputing, 423, 551-571. https://doi.org/10.1016/j.neucom.2020.10.068
  • Gutiérrez, L., Melin, P., & Lopez, M. (2010, July). Modular neural network integrator for human recognition from ear images. In The 2010 International Joint Conference on Neural Networks (IJCNN), 1-5. DOI: 10.1109/IJCNN.2010.5596633
  • Haghighat, M. B. A., Aghagolzadeh, A., & Seyedarabi, H. (2011). Multi-focus image fusion for visual sensor networks in DCT domain. Computers & Electrical Engineering, 37(5), 789-797. https://doi.org/10.1016/j.compeleceng.2011.04.016
  • Jain, A. K., Flynn, P., & Ross, A. A. (Eds.). (2007). Handbook of biometrics. Springer Science & Business Media.
  • Jamil, N., AlMisreb, A., & Halin, A. A. (2014). Illumination-invariant ear authentication. Procedia Computer Science, 42, 271-278. https://doi.org/10.1016/j.procs.2014.11.062
  • Kong, S. G., Heo, J., Boughorbel, F., Zheng, Y., Abidi, B. R., Koschan, A., Yi, M., & Abidi, M. A. (2007). Multiscale fusion of visible and thermal IR images for illumination-invariant face recognition. International Journal of Computer Vision, 71(2), 215-233. https://doi.org/10.1007/s11263-006-6655-0
  • Lannarelli, A. (1989). Ear identification. Forensic identification series.
  • Ma, Y., Huang, Z., Wang, X., & Huang, K. (2020). An overview of multimodal biometrics using the face and ear. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/6802905
  • Maity, S., Abdel-Mottaleb, M., & Asfour, S. S. (2020). Multimodal biometrics recognition from facial video with missing modalities using deep learning. Journal of Information Processing Systems, 16(1), 6-29. DOI: 10.3745/JIPS.02.0129
  • Moreno, B., Sanchez, A., & Vélez, J. F. (1999, October). On the use of outer ear images for personal identification in security applications. In Proceedings IEEE 33rd Annual 1999 International Carnahan Conference on Security Technology (Cat. No. 99CH36303), 469-476. DOI: 10.1109/CCST.1999.797956
  • Morlet, J., Arens, G., Fourgeau, E., & Glard, D. (1982). Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media. Geophysics, 47(2), 203-221. https://doi.org/10.1190/1.1441328
  • Nejati, H., Zhang, L., Sim, T., Martinez-Marroquin, E., & Dong, G. (2012, November). Wonder ears: Identification of identical twins from ear images. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 1201-1204.
  • Pajares, G., & De La Cruz, J. M. (2004). A wavelet-based image fusion tutorial. Pattern recognition, 37(9), 1855-1872. https://doi.org/10.1016/j.patcog.2004.03.010
  • Pflug, A., Paul, P. N., & Busch, C. (2014, October). A comparative study on texture and surface descriptors for ear biometrics. In 2014 International carnahan conference on security technology (ICCST), 1-6. DOI: 10.1109/CCST.2014.6986993
  • Rane, M. E., & Bhadade, U. (2020, December). Face and palmprint Biometric recognition by using weighted score fusion technique. In 2020 IEEE Pune Section International Conference (PuneCon), 11-16. DOI: 10.1109/PuneCon50868.2020.9362433
  • Sarangi, P. P., Mishra, B. P., & Dehuri, S. (2018, March). Multimodal biometric recognition using human ear and profile face. In 2018 4th International Conference on Recent Advances in Information Technology (RAIT), 1-6. DOI: 10.1109/RAIT.2018.8389035
  • Sarangi, P. P., Nayak, D. R., Panda, M., & Majhi, B. (2022). A feature-level fusion based improved multimodal biometric recognition system using ear and profile face. Journal of Ambient Intelligence and Humanized Computing, 13(4), 1867-1898. https://doi.org/10.1007/s12652-021-02952-0
  • Seal, A., Bhattacharjee, D., Nasipuri, M., Gonzalo-Martin, C., & Menasalvas, E. (2017). Fusion of visible and thermal images using a directed search method for face recognition. International Journal of Pattern Recognition and Artificial Intelligence, 31(04), 1756005. https://doi.org/10.1142/S0218001417560055
  • Singh, S., Gyaourova, A., Bebis, G., & Pavlidis, I. (2004, August). Infrared and visible image fusion for face recognition. In Biometric technology for human identification, 5404, 585-596. https://doi.org/10.1117/12.543549
  • Starck, J. L., Donoho, D. L., & Candès, E. J. (2003). Astronomical image representation by the curvelet transform. Astronomy & Astrophysics, 398(2), 785-800. DOI: 10.1051/0004-6361:20021571
  • Toygar, Ö., Alqaralleh, E., & Afaneh, A. (2018). Person identification using multimodal biometrics under different challenges. Human-Robot Interaction-Theory and Application, 81-96. DOI: 10.5772/intechopen.71667
  • Victor, B., Bowyer, K., & Sarkar, S. (2002, August). An evaluation of face and ear biometrics. In 2002 International Conference on Pattern Recognition, 1, 429-432. DOI: 10.1109/ICPR.2002.1044746
  • Yu, H., Yang, L. T., Zhang, Q., Armstrong, D., & Deen, M. J. (2021). Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing, 444, 92-110. https://doi.org/10.1016/j.neucom.2020.04.157
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Görüntü İşleme, Örüntü Tanıma, Derin Öğrenme
Bölüm Elektrik Elektronik Mühendisliği
Yazarlar

Mücahit Cihan 0000-0002-1426-319X

Murat Ceylan 0000-0001-6503-9668

Yayımlanma Tarihi 3 Aralık 2023
Gönderilme Tarihi 17 Ağustos 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Cihan, M., & Ceylan, M. (2023). IMAGE FUSION AND DEEP LEARNING BASED EAR RECOGNITION USING THERMAL AND VISIBLE IMAGES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(4), 997-1009. https://doi.org/10.17780/ksujes.1345020