Transfer Learning with Optimization Enabled Person Recognition using Ear Images
Keywords:
Convolutional Neural Networks, Ear image, Person recognition, Transfer learningAbstract
The human ear is a good source of data for passive human recognition as it doesn't need the assistance of the individual whose identity we're trying to determine and because the ear's structure doesn't vary significantly over time. A hybrid method for person detection utilizing ear pictures called Gannet Sparrow Search Optimization _enabled Convolutional Neural Network with Transfer Learning (GSSO_CNN-TL) is created to counteract these constraints. The input ear image is initially attained from an ear recognition dataset and fed to pre-processing stage, where external noise are removed using Non-Local Means (NLM) filter. After that, techniques including rotation, flipping, cropping, and colour augmentation are applied to the pre-processed image. Then features like Scale-invariant feature transform (SIFT), Speeded up robust features (SURF), Pyramid Histogram of Oriented Gradients (PHoG), and Local Binary Pattern (LBP) are retrieved. Lastly, person identification is done using CNN with TL, where CNN is employed with hyper parameters from the trained models, namely deep batch-normalized eLU AlexNet (DbneAlexNet). Nonetheless, the GSSO is efficiently used to train classifier. By merging Gannet Optimization Algorithm (GOA) and Sparrow Search Algorithm (SSA), the created GSSO is produced. Furthermore, the devised approach achieved maximum precision, recall, and f-measure of 92.6%, 95.8%, and 94.2% respectively.
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AhilaPriyadharshini, R., Arivazhagan, S. and Arun, M., “A deep learning approach for person identification using ear biometrics”, Applied intelligence, vol. 51, pp.2161-2172, 2021.
Hossain, S., Sultana Mitu, S., Afrin, S. and Akhter, S., “A Real-time Machine Learning-Based Person Recognition System with Ear Biometrics”, International Journal of Computing and Digital System, 2021.
Mursalin, M., Ahmed, M. and Haskell-Dowland, P., “Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model”, Sensors, vol. 22, no. 22, pp.8988, 2022.
Alshazly, H., Linse, C., Barth, E. and Martinetz, T., “Deep convolutional neural networks for unconstrained ear recognition”, IEEE Access, vol. 8, pp.170295-170310, 2020.
Sarangi, P.P., Mishra, B.S.P., Dehuri, S. and Cho, S.B., “An evaluation of ear biometric system based on enhanced Jaya algorithm and SURF descriptors”, Evolutionary Intelligence, vol. 13, pp.443-461, 2020.
Sinha, H., Manekar, R., Sinha, Y. and Ajmera, P.K., “Convolutional neural network-based human identification using outer ear images”, In Soft Computing for Problem Solving: SocProS 2017, Volume 2 (pp. 707-719), Springer Singapore, 2019.
Alshazly, H., Linse, C., Barth, E. and Martinetz, T., “Ensembles of deep learning models and transfer learning for ear recognition”, Sensors, vol. 19, no. 19, pp.4139, 2019.
Yaman, D., Eyiokur, F.I. and Ekenel, H.K., “Ear2Face: Deep Biometric Modality Mapping”, arXiv preprint arXiv:2006.01943, 2020.
Nejati, H., Zhang, L., Sim, T., Martinez-Marroquin, E. and Dong, G., “Wonder ears: Identification of identical twins from ear images”, In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 1201-1204, IEEE, November, 2012.
Sharkas, M., “Ear recognition with ensemble classifiers; A deep learning approach”, Multimedia Tools and Applications, pp.1-27, 2022.
Zhu, Q. and Mu, Z., “PointNet++ and three layers of features fusion for occlusion three-dimensional ear recognition based on one sample per person”, Symmetry, vol. 12, no. 1, pp.78, 2020.
Unar, J.A., Seng, W.C. and Abbasi, A., “A review of biometric technology along with trends and prospects”, Pattern recognition, vol. 47, no. 8, pp.2673-2688, 2014.
Wang, Z., Yang, J. and Zhu, Y., “Review of ear biometrics”, Archives of Computational Methods in Engineering, vol. 28, pp.149-180, 2021.
Ganapathi, I.I., Ali, S.S., Prakash, S., Vu, N.S. and Werghi, N., “A Survey of 3D Ear Recognition Techniques”, ACM Computing Surveys, vol. 55, no. 10, pp.1-36, 2023.
Zhao, W., Chellappa, R., Phillips, P.J. and Rosenfeld, A., “Face recognition: A literature survey”, ACM computing surveys (CSUR), vol. 35, no. 4, pp.399-458, 2023.
Minaee, S., Abdolrashidi, A., Su, H., Bennamoun, M. and Zhang, D., “Biometrics recognition using deep learning: A survey”, Artificial Intelligence Review, pp.1-49, 2023.
Datasets for ear detection and recognition, "https://www.kaggle.com/datasets/omarhatif/datasets-for-ear-detection-and-recognition", accessed on April, 2023.
Kim, J.H., Lee, C., Sim, J.Y. and Kim, C.S., "Single-image deraining using an adaptive nonlocal means filter", In IEEE international conference on image processing, pp. 914-917, September 2013.
Pan, J.S., Zhang, L.G., Wang, R.B., Snasel, V. and Chu, S.C., "Gannet optimization algorithm: A new metaheuristic algorithm for solving engineering optimization problems", Mathematics and Computers in Simulation, vol.202, pp.343-373, 2022.
Xue, J. and Shen, B., "A novel swarm intelligence optimization approach: sparrow search algorithm", Systems science & control engineering, vol.8, no.1, pp.22-34, 2020.
Khosla, C. and Saini, B.S., “Enhancing performance of deep learning models with different data augmentation techniques: A survey”, In 2020 International Conference on Intelligent Engineering and Management (ICIEM), pp. 79-85, IEEE, June, 2020.
Ghoualmi, L., Draa, A. and Chikhi, S., “An ear biometric system based on artificial bees and the scale invariant feature transform”, Expert Systems with Applications, vol. 57, pp.49-61, 2016.
Bai, Y., Guo, L., Jin, L. and Huang, Q., “A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition”, In proceedings International Conference on Image Processing (ICIP), pp. 3305-3308, November, 2009.
Lu, K., Luo, J., Zhong, Y. and Chai, X., “Identification of wool and cashmere SEM images based on SURF features”, Journal of engineered fibers and fabrics, vol. 14, pp.1558925019866121, 2019.
NarainPonraj, D., Christy, E., Aneesha, G., Susmitha, G. and Sharu, M., “Analysis of LBP and LOOP based textural feature extraction for the classification of CT Lung images”, In 2018 4th International Conference on Devices, Circuits and Systems (ICDCS), pp. 309-312, IEEE, March, 2018.
Askarzadeh, A., “A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm”, Computers & structures, vol. 169, pp.1-12, 2016.
Zheng, T. and Luo, W., “An improved squirrel search algorithm for optimization”, Complexity, 2019.
Ouyang, C., Zhu, D. and Wang, F., “A learning sparrow search algorithm”, Computational intelligence and neuroscience, 2021.
Yao, R., Wang, N., Liu, Z., Chen, P. and Sheng, X., “Intrusion detection system in the advanced metering infrastructure: a cross-layer feature-fusion CNN-LSTM-based approach”, Sensors, vol. 21, no. 2, pp.626, 2021.
Shi, Z., Hao, H., Zhao, M., Feng, Y., He, L., Wang, Y. and Suzuki, K., “A deep CNN based transfer learning method for false positive reduction”, Multimedia Tools and Applications, vol. 78, pp.1017-1033, 2019.
Alaeddine, H. and Jihene, M., “Deep Batch-normalized eLU AlexNet for Plant Diseases Classification”, In 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 17-22, IEEE, March, 2021.
Imoto, K., Nakai, T., Ike, T., Haruki, K. and Sato, Y., “A CNN-based transfer learning method for defect classification in semiconductor manufacturing”, IEEE Transactions on Semiconductor Manufacturing, vol. 32, no. 4, pp.455-459, 2019.
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