과제정보
The author would like to express their cordial thanks to the deanship of scientific research at Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
참고문헌
- M. Martinez, "Recognizing Imprecisely Localized, Partially Occluded and Expression Variant Faces from a Single Sample per Class", in IEEE Trans. on PAMI, V.24, p748-763, 2002 https://doi.org/10.1109/TPAMI.2002.1008382
- P. Quintiliano, A. N. Santa-Rosa, R. Guadagnin "Face recognition based on eigenfeatures", Proc. SPIE Vol. 4550, p. 140-145, Image Extraction, Segmentation, and Recognition, 2001
- M. A. Turk, A. P. Pentland, "Face recognition using eigenfaces", Proceedings of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 586-591, Hawaii 1991.
- J. Kim, Y. Sung, S.M. Yoon and B.G. Park, "A New Video Surveillance System Employing Occluded Face Detection", Proc. of the 18th int. conf. on Innovations in Applied Artificial Intelligence, Italy, pp. 65-68, 2005.
- S. M. Yoon and S. C. Kee, "Detection of Partially Occluded Face using Support Vector Machines", IAPR Workshop on Machine Vision Applications, Japan, pp. 546-549, 2002.
- K. Hotta, "A Robust Face Detector Under Partial Occlusion", Int. Conf. on Image Processing, 2004 (ICIP '04), pp. 597-600, 2004.
- T. Kurita, M. Pic and T. Takahashi, "Recognition and Detection of Occluded Faces by a Neural Network Classifier with Recursive Data Reconstruction", Proc. of the IEEE Conf. on Advanced Video and Signal Based Surveillance (AVSS'03), pp. 53-58, 2003.
- M. A. Mottaleb and A. Elgammal. Face Detection in complex environments from color images. IEEE ICIP: 622-626, 1999.
- J. Ross and Beveridge et. al. A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 535 - 542, 2001.
- A.M. Martinez and R. Benavente, "The AR Face Database", CVC Technical Report 24, 1998.
- A.M. Martinez, "Recognition of Partially Occluded and/or Imprecisely Localized Faces using a Probabilistic Approach", CVPR, Vol. 1, pp. 712-717, 2000. MIT face database, ftp://whitechapel.media.mit.edu/pub/images/
- A.S. Georghiades, P.N. Belhumeur, and D.J. Kriegman, "From few to many: Illumination cone models for recognition under variable lighting and pose", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no.6, pp. 643-660, 2001 https://doi.org/10.1109/34.927464
- Abate A., Nappi M., Riccio D., and Tucci M., "Occluded Face Recognition by Means of the IFS," in Proceedings of the 2nd International Conference Image Analysis and Recognition, Berlin, pp. 1073-1080, 2005.
- Aleix M., "Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample Per Class," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 748-763, 2002. https://doi.org/10.1109/TPAMI.2002.1008382
- Amirhosein N., Esam A., and Majid A., "Illumination Invariant Feature Extraction and Mutual-Information-Based Local Matching for Face Recognition under Illumination Variation and Occlusion," Pattern Recognition, vol. 44, no. 10-11, pp. 2576-2587, 2011. https://doi.org/10.1016/j.patcog.2011.03.012
- Baudat G. and Anouar F., "Generalized Discriminant Analysis using a Kernel Approach," Neural Computation, vol. 12, no. 10, pp. 2385-2404, 2000. https://doi.org/10.1162/08997660030001498
- Sharma, M., Prakash, S., & Gupta, P. (2013). An efficient partial occluded face recognition system. Neurocomputing, 116, 231-241. https://doi.org/10.1016/j.neucom.2011.12.063
- Li, X. X., Dai, D. Q., Zhang, X. F., & Ren, C. X. (2013). Structured sparse error coding for face recognition with occlusion. IEEE transactions on image processing, 22(5), 1889-1900. https://doi.org/10.1109/TIP.2013.2237920
- Ou, W., You, X., Tao, D., Zhang, P., Tang, Y., & Zhu, Z. (2014). Robust face recognition via occlusion dictionary learning. Pattern Recognition, 47(4), 1559-1572. https://doi.org/10.1016/j.patcog.2013.10.017
- Chen W., Yuen P., Huang J., and Dai D., "Kernel Machine-Based One-Parameter Regularized Fisher Discriminant Method for Face Recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 35, no. 4, pp. 659-669, 2005. https://doi.org/10.1109/TSMCB.2005.844596
- Yu, Y. F., Dai, D. Q., Ren, C. X., & Huang, K. K. (2017). Discriminative multi-scale sparse coding for single-sample face recognition with occlusion. Pattern Recognition, 66, 302-312. https://doi.org/10.1016/j.patcog.2017.01.021
- Wu, C. Y., & Ding, J. J. (2018). Occluded face recognition using low-rank regression with generalized gradient direction. Pattern Recognition, 80, 256-268. https://doi.org/10.1016/j.patcog.2018.03.016
- Long, Y., Zhu, F., Shao, L., & Han, J. (2018). Face recognition with a small occluded training set using spatial and statistical pooling. Information Sciences, 430, 634-644. https://doi.org/10.1016/j.ins.2017.10.042
- Zheng, W., Gou, C., & Wang, F. Y. (2020). A novel approach inspired by optic nerve characteristics for few-shot occluded face recognition. Neurocomputing, 376, 25-41. https://doi.org/10.1016/j.neucom.2019.09.045
- Kumar, S., & Singh, S. K. (2020). Occluded Thermal Face Recognition Using Bag of CNN ($ Bo $ CNN). IEEE Signal Processing Letters, 27, 975-979. https://doi.org/10.1109/LSP.2020.2996429
- Qiu, H., Gong, D., Li, Z., Liu, W., & Tao, D. (2021). End2End Occluded Face Recognition by Masking Corrupted Features. IEEE Transactions on Pattern Analysis and Machine Intelligence.
- Bommidi, K., & Sundaramurthy, S. (2021). A compressed string matching algorithm for face recognition with partial occlusion. Multimedia Systems, 27(2), 191-203. https://doi.org/10.1007/s00530-020-00727-9
- Koc, M. (2021). A novel partition selection method for modular face recognition approaches on occlusion problem. Machine Vision and Applications, 32(1), 1-11. https://doi.org/10.1007/s00138-020-01119-9