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Hand Vein Recognition via Discriminative Convolutional Sparse Coding | ||
| Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 2، شماره 1 - شماره پیاپی 7، مرداد 2022، صفحه 27-35 اصل مقاله (749.51 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2022.25500.1088 | ||
| نویسندگان | ||
| Ali Nozaripour* 1؛ Hadi Soltanizadeh2 | ||
| 1Department of Electrical and Computer Engineering Hakim University, Sabzevar,Iran. | ||
| 2Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. | ||
| تاریخ دریافت: 15 آذر 1400، تاریخ بازنگری: 17 فروردین 1401، تاریخ پذیرش: 21 شهریور 1401 | ||
| چکیده | ||
| Personal identification based on vein pattern is one of the latest biometric approaches that have attracted lots of attention. Besides, Convolutional Sparse Coding (CSC) is a popular model in the signal and image processing communities, resolving some limitations of the traditional patch-based sparse representations. As most existing CSC algorithms are suited for image restoration, we present a novel discriminative model based on CSC for dorsal hand vein recognition in this paper. The proposed method, discriminative local block coordinate descent (D-LoBCoD), is based on extending the LoBCoD algorithm by incorporating the classification error into the objective function that considers the performance of a linear classifier and the representational power of the filters simultaneously. Thus, for training, in each iteration, after updating the sparse coefficients and convolutional filters, we minimize the classification error by updating the classifier’s parameters according to the label information. Finally, after training, the label of the query image will be determined by the trained classifier. One thousand two hundred dorsal hand vein images taken from 100 individuals are used to verify the validity of the proposed methods. The experimental results show that our method outperforms other competing methods. Further, we demonstrate that our proposed method is less dependent on the number of training samples because of capturing more representative information from the corresponding images. | ||
| کلیدواژه‌ها | ||
| Convolutional Sparse Coding؛ Dorsal hand vein pattern؛ Image classification؛ Region of interest؛ Sparse representation | ||
| مراجع | ||
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[1] Wang, Z. Pan, G. Wang, M. Li, and Y. Li, “Spatial pyramid pooling of selective convolutional features for vein recognition,” IEEE Access, vol. 6, pp. 28563-28572, 2018.
[2] Shazeeda and B. A. Rosdi, “Finger vein recognition using mutual sparse representation classification,” IET Biometrics, vol. 8, no. 1, pp. 49-58, 2019.
[3] Shaaban, “Enhanced Region of Interest Extraction method for Finger Vein Images,” Artificial Intelligence & Robotics Development Journal, pp. 13-25, 2021.
[4] N. Pour, E. Eslami, and J. Haddadnia, “A new method for automatic extraction of region of interest from infrared images of dorsal hand vein pattern based on floating selection model,” International Journal of Applied Pattern Recognition, vol. 2, no. 2, pp. 111-127, 2015.
[5] Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE transactions on pattern analysis and machine intelligence, vol. 31, no. 2, pp. 210-227, 2008.
[6] Jiang, Z. Lin, and L. S. Davis, “Label consistent K-SVD: Learning a discriminative dictionary for recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 11, pp. 2651-2664, 2013.
[7] Yang, L. Zhang, X. Feng, and D. Zhang, “Sparse representation based fisher discrimination dictionary learning for image classification,” International Journal of Computer Vision, vol. 109, no. 3, pp. 209-232, 2014.
[8] Gou, L. Wang, Z. Yi, Y. Yuan, W. Ou, and Q. Mao, “Weighted discriminative collaborative, competitive representation for robust image classification,” Neural Networks, vol. 125, pp. 104-120, 2020.
[9] Li, Z. Zhang, J. Qin, Z. Zhang, and L. Shao, “Discriminative fisher embedding dictionary learning algorithm for object recognition,” IEEE transactions on neural networks and learning systems, vol. 31, no. 3, pp. 786-800, 2019.
[10] H. Vu and V. Monga, “Fast low-rank shared dictionary learning for image classification,” IEEE Transactions on Image Processing, vol. 26, no. 11, pp. 5160-5175, 2017.
[11] Zhou, H. Jiang, L. Gong, and X. Xie, “Double-image compression and encryption algorithm based on co-sparse representation and random pixel exchanging,” Optics and Lasers in Engineering, vol. 110, pp. 72-79, 2018.
[12] Pan, Z. Jing, L. Qiao, and M. Li, “Discriminative structured dictionary learning on Grassmann manifolds and its application on image restoration,” IEEE transactions on cybernetics, vol. 48, no. 10, pp. 2875-2886, 2017.
[13] Miandji, S. Hajisharif, and J. Unger, “A unified framework for compression and compressed sensing of light fields and light field videos,” ACM Transactions on Graphics (TOG), vol. 38, no. 3, pp. 1-18, 2019.
[14] Ma, T.-Z. Huang, J. Huang, and C.-C. Zheng, “Local low-rank and sparse representation for hyperspectral image denoising,” IEEE Access, vol. 7, pp. 79850-79865, 2019.
[15] Ding, M. Shao, and Y. Fu, “Deep, robust encoder through locality preserving low-rank dictionary,” in European Conference on Computer Vision, 2016: Springer, pp. 567-582.
[16] Xu, Z. Li, J. Yang, and D. Zhang, “A survey of dictionary learning algorithms for face recognition,” IEEE Access, vol. 5, pp. 8502-8514, 2017.
[17] Zisselman, J. Sulam, and M. Elad, “A local block coordinate descent algorithm for the CSC model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 8208-8217.
[18] Wang, Q. Yao, J. T. Kwok, and L. M. Ni, “Scalable online convolutional sparse coding,” IEEE Transactions on Image Processing, vol. 27, no. 10, pp. 4850-4859, 2018.
[19] Romano and M. Elad, “Patch-disagreement as a way to improve K-SVD denoising,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015: IEEE, pp. 1280-1284.
[20] Rodríguez, “FAST CONVOLUTIONAL SPARSE CODING WITH ℓ 0 PENALTY,” in 2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), 2018: IEEE, pp. 1-4.
[21] Papyan, Y. Romano, J. Sulam, and M. Elad, “Convolutional dictionary learning via local processing,” in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 5296-5304.
[22] He, L. Yu, Z. Liu, and W. Yang, “Image super-resolution by learning weighted convolutional sparse coding,” Signal, Image and Video Processing, pp. 1-9, 2021.
[23] Heide, W. Heidrich, and G. Wetzstein, “Fast and flexible convolutional sparse coding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5135-5143.
[24] Chang, J. Han, C. Zhong, A. M. Snijders, and J.-H. Mao, “Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications,” IEEE transactions on pattern analysis and machine intelligence, vol. 40, no. 5, pp. 1182-1194, 2017.
[25] Wang et al., “Multimodal medical image fusion based on nonsubsampled shearlet transform and convolutional sparse representation,” Multimedia Tools and Applications, pp. 1-21, 2021.
[26] Cogliati, Z. Duan, and B. Wohlberg, “Context-dependent piano music transcription with convolutional sparse coding,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no. 12, pp. 2218-2230, 2016.
[27] -W. Liao and L. Su, “Monaural source separation using ramanujan subspace dictionaries,” IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1156-1160, 2018.
[28] Šorel and F. Šroubek, “Fast convolutional sparse coding using matrix inversion lemma,” Digital Signal Processing, vol. 55, pp. 44-51, 2016.
[29] Wohlberg, “Efficient algorithms for convolutional sparse representations,” IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 301-315, 2015.
[30] Boyd, N. Parikh, and E. Chu, Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc, 2011.
[31] Degraux, U. S. Kamilov, P. T. Boufounos, and D. Liu, “Online convolutional dictionary learning for multimodal imaging,” in 2017 IEEE International Conference on Image Processing (ICIP), 2017: IEEE, pp. 1617-1621.
[32] Liu, C. Garcia-Cardona, B. Wohlberg, and W. Yin, “Online convolutional dictionary learning,” in 2017 IEEE International Conference on Image Processing (ICIP), 2017: IEEE, pp. 1707-1711.
[33] Kavukcuoglu, P. Sermanet, Y.-L. Boureau, K. Gregor, M. Mathieu, and Y. Cun, “Learning convolutional feature hierarchies for visual recognition,” Advances in neural information processing systems, vol. 23, pp. 1090-1098, 2010.
[34] Zhou, H. Chang, K. Barner, P. Spellman, and B. Parvin, “Classification of histology sections via multispectral convolutional sparse coding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 3081-3088.
[35] Chen, J. Li, B. Ma, and G. Wei, “Convolutional sparse coding classification model for image classification,” in 2016 IEEE international conference on image processing (ICIP), 2016: IEEE, pp. 1918-1922.
[36] Jin and C. P. Chen, “Convolutional sparse coding for face recognition,” in 2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS), 2017: IEEE, pp. 137-141.
[37] Chen, S. A. Billings, and W. Luo, “Orthogonal least squares methods and their application to non-linear system identification,” International Journal of Control, vol. 50, no. 5, pp. 1873-1896, 1989.
[38] S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Review, vol. 43, no. 1, pp. 129-159, 2001.
[39] -S. Pham and S. Venkatesh, “Joint learning and dictionary construction for pattern recognition,” in 2008 IEEE conference on computer vision and pattern recognition, 2008: IEEE, pp. 1-8.
[40] Yuksel, L. Akarun, and B. Sankur, “Hand vein biometry based on geometry and appearance methods,” IET computer vision, vol. 5, no. 6, pp. 398-406, 2011. [41] Nozaripour and H. Soltanizadeh, “Robust Vein Recognition against Rotation Using Kernel Sparse Representation,” Journal of AI and Data Mining, 2021.
[42] -L. Lin, S.-H. Wang, H.-Y. Cheng, K.-C. Fan, W.-L. Hsu, and C.-R. Lai, “Bimodal biometric verification using the fusion of palmprint and infrared palm-dorsum vein images,” Sensors, vol. 15, no. 12, pp. 31339-31361, 2015.
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