Implementation of dynamic and fast mining algorithms on incremental datasets to discover qualitative rules

• Autorzy: Naresh Pannangi , Suguna R.

Identyfikatory

DOI

10.23743/acs-2021-23

• Języki publikacji: EN

Abstrakty

EN

Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition algorithms’ performance using RGB-D images. These algorithms calculate the descriptor which uses RGB and Depth map faces based on local binary pattern. Those images are also tested for the fusion of LBP and DCT methods. The fusion of LBP and DCT approach produces a recognition rate of 97.5% during the experiment

Słowa kluczowe

EN

frequent itemset; nodeset; FIN and IPOC

PL

częste itemy; nodeset; FIN i IPOC

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2021
• Tom: Vol. 17, no 3
• Strony: 82--91
• Opis fizyczny: Bibliogr. 18 poz., fig., tab.

Twórcy

autor

Naresh Pannangi

• Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai

• https://orcid.org/0000-0003-2932-6699

autor

Suguna R.

• Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai

• https://orcid.org/0000-0002-8930-9092

Bibliografia

• [1] Abebe, H. B., & Hwang, C. L. (2019). RGB-D face recognition using LBP with suitable feature dimension of depth image. IET Cyber-Physical Systems: Theory & Applications, 4(3), 189–197. https://doi.org/10.1049/ietcps.2018.5045
• [2] Chen, P. Z., & Chen, S. L. (2010). A new face recognition algorithm based on dct and lbp. In Quantitative Logic and Soft Computing 2010 (pp. 811–818). Springer. https://doi.org/10.1007/978-3-642-15660-1_82
• [3] Chowdhury, A., & Vatsa, M. (2016). RGB-D face recognition in surveillance videos (Doctoral dissertation). Retrieved from https://repository.iiitd.edu.in/jspui/handle/123456789/440
• [4] Cruz, L., Lucio, D., & Velho, L. (2012). Kinect and rgbd images: Challenges and applications. In 2012 25th SIBGRAPI conference on graphics, patterns and images tutorials(pp. 36–49). IEEE. https://doi.org/10.1109/SIBGRAPIT.2012.13
• [5] Goswami, G., Vatsa, M., & Singh, R. (2014). RGB-D face recognition with texture and attribute features. IEEE Transactions on Information Forensics and Security, 9(10), 1629–1640. https://doi.org/10.1109/TIFS.2014.2343913
• [6] Han, J., Shao, L., Xu, D., & Shotton, J. (2013). Enhanced computer vision with microsoft kinect sensor: A review. IEEE transactions on cybernetics, 43(5), 1318–1334. https://doi.org/10.1109/TCYB.2013.2265378
• [7] Hg, R. I., Jasek, P., Rofidal, C., Nasrollahi, K., Moeslund, T. B., & Tranchet, G. (2012). An rgb-d database using microsoft's kinect for windows for face detection. In 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems (pp. 42–46). IEEE. https://doi.org/10.1109/SITIS.2012.17
• [8] Hsu, G. S. J., Liu, Y. L., Peng, H. C., & Wu, P. X. (2014). RGB-D-based face reconstruction and recognition. IEEE Transactions on Information Forensics and Security, 9(12), 2110–2118. https://doi.org/10.1109/TIFS.2014.2361028
• [9] Huynh, T., Min, R., & Dugelay, J. L. (2012). An efficient LBP-based descriptor for facial depth images applied to gender recognition using RGB-D face data. In Asian Conference on Computer Vision (pp. 133–145). Springer. https://doi.org/10.1007/978-3-642-37410-4_12
• [10] Lin, D., Fidler, S., & Urtasun, R. (2013). Holistic scene understanding for 3d object detection with rgbd cameras. In Proceedings of the IEEE international conference on computer vision (pp. 1417–1424). IEEE. https://doi.org/10.1109/ICCV.2013.179
• [11] Min, R., Kose, N., & Dugelay, J. L. (2014). Kinectfacedb: A kinect database for face recognition. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(11), 1534–1548. https://doi.org/10.1109/TSMC.2014.2331215
• [12] Shermina, J. (2011). Illumination invariant face recognition using discrete cosine transform and principal component analysis. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 826–830). IEEE. https://doi.org/10.1109/ICETECT.2011.5760233
• [13] Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012). Indoor segmentation and support inference from rgbd images. In European conference on computer vision (pp. 746-760). Springer. https://doi.org/10.1007/978-3- 642-33715-4_54
• [14] Song, K., Yan, Y., Zhao, Y., & Liu, C. (2015). Adjacent evaluation of local binary pattern for texture classification. Journal of Visual Communication and Image Representation, 33, 323–339. https://doi.org/10.1016/j.jvcir.2015.09.016
• [15] Wang, J., Liu, Z., Chorowski, J., Chen, Z., & Wu, Y. (2012). Robust 3d action recognition with random occupancy patterns. In European Conference on Computer Vision (pp. 872–885). Springer. https://dl.acm.org/doi/10.5555/2964398.2964463
• [16] Yu, W., Gan, L., Yang, S., Ding, Y., Jiang, P., Wang, J., & Li, S. (2014). An improved LBP algorithm for texture and face classification. Signal, Image and Video Processing, 8(1), 155–161. https://doi.org/10.1007/s11760-014- 0652-5
• [17] Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4), 399–458. https://doi.org/10.1145/954339.954342
• [18] Zohra, F. T., Rahman, M. W., & Gavrilova, M. (2016). Occlusion detection and localization from Kinect depth images. In 2016 International Conference on Cyberworlds (CW) (pp. 189–196). IEEE. https://doi.org/10.1109/CW.2016.40

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).