RGB-D FACE RECOGNITION USING LBP-DCT ALGORITHM
Sunil Kumar B L
blsuny@gmail.comCanara Engineering College (India)
Sharmila Kumari M
PA College of Engineering (India)
Abstract
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.
Keywords:
RGB-D, Kinect, Local Binary Pattern, Pattern Recognition, Feature Extraction, Histogram, Face RecognitionReferences
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
DOI: https://doi.org/10.1049/iet-cps.2018.5045
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-642-15660-1_82
Google Scholar
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
Google Scholar
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
DOI: https://doi.org/10.1109/SIBGRAPI-T.2012.13
Google Scholar
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
DOI: https://doi.org/10.1109/TIFS.2014.2343913
Google Scholar
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
DOI: https://doi.org/10.1109/TCYB.2013.2265378
Google Scholar
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
DOI: https://doi.org/10.1109/SITIS.2012.17
Google Scholar
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
DOI: https://doi.org/10.1109/TIFS.2014.2361028
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-642-37410-4_12
Google Scholar
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
DOI: https://doi.org/10.1109/ICCV.2013.179
Google Scholar
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
DOI: https://doi.org/10.1109/TSMC.2014.2331215
Google Scholar
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
DOI: https://doi.org/10.1109/ICETECT.2011.5760233
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-642-33715-4_54
Google Scholar
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
DOI: https://doi.org/10.1016/j.jvcir.2015.09.016
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-642-33709-3_62
Google Scholar
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
DOI: https://doi.org/10.1007/s11760-014-0652-5
Google Scholar
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
DOI: https://doi.org/10.1145/954339.954342
Google Scholar
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
DOI: https://doi.org/10.1109/CW.2016.40
Google Scholar
Authors
Sharmila Kumari MPA College of Engineering India
Statistics
Abstract views: 182PDF downloads: 44
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Qingyu Liu, Roben A. Juanatas, MASK FACE INPAINTING BASED ON IMPROVED GENERATIVE ADVERSARIAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Sahar ZAMANI KHANGHAH, Keivan MAGHOOLI, EMOTION RECOGNITION FROM HEART RATE VARIABILITY WITH A HYBRID SYSTEM COMBINED HIDDEN MARKOV MODEL AND POINCARE PLOT , Applied Computer Science: Vol. 20 No. 1 (2024)
- Manikandan SRIDHARAN, Delphin Carolina RANI ARULANANDAM, Rajeswari K CHINNASAMY, Suma THIMMANNA, Sivabalaselvamani DHANDAPANI, RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING , Applied Computer Science: Vol. 17 No. 2 (2021)
- Nawazish NAVEED, Hayan T. MADHLOOM, Mohd Shahid HUSAIN, BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 17 No. 3 (2021)
- Pascal Krutz, Matthias Rehm, Holger Schlegel, Martin Dix, RECOGNITION OF SPORTS EXERCISES USING INERTIAL SENSOR TECHNOLOGY , Applied Computer Science: Vol. 19 No. 1 (2023)
- Mohanad ABDULHAMID, Njagi KINYUA, SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE , Applied Computer Science: Vol. 16 No. 1 (2020)
- Mohamed ELBAHRI, Nasreddine TALEB, Sid Ahmed El Mehdi ARDJOUN, Chakib Mustapha Anouar ZOUAOUI , FEW-SHOT LEARNING WITH PRE-TRAINED LAYERS INTEGRATION APPLIED TO HAND GESTURE RECOGNITION FOR DISABLED PEOPLE , Applied Computer Science: Vol. 20 No. 2 (2024)
- Xianlei GE, Vladimir MARIANO, RETRACTED PAPER: Enhancing 3D human pose estimation through multi-feature fusion , Applied Computer Science: Vol. 19 No. 3 (2023)
- Nancy WOODS, Gideon BABATUNDE, A ROBUST ENSEMBLE MODEL FOR SPOKEN LANGUAGE RECOGNITION , Applied Computer Science: Vol. 16 No. 3 (2020)
- Lei Liu, Eric B. Blancaflor, Mideth Abisado, A LIGHTWEIGHT MULTI-PERSON POSE ESTIMATION SCHEME BASED ON JETSON NANO , Applied Computer Science: Vol. 19 No. 1 (2023)
You may also start an advanced similarity search for this article.
