Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess RGB-D face recognition using LBP with suitable feature dimension of depth image

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 23/07/2018, Accepted 31/01/2019, Revised 27/01/2019, Published 01/02/2019

This study proposes a robust method for the face recognition from low-resolution red, green, and blue-depth (RGB-D) cameras acquired images which have a wide range of variations in head pose, illumination, facial expression, and occlusion in some cases. The local binary pattern (LBP) of the RGB-D images with the suitable feature dimension of Depth image is employed to extract the facial features. On the basis of error correcting output codes, they are fed to multiclass support vector machines (MSVMs) for the off-line training and validation, and then the online classification. The proposed method is called as the LBP-RGB-D-MSVM with the suitable feature dimension of the depth image. The effectiveness of the proposed method is evaluated by the four databases: Indraprastha Institute of Information Technology, Delhi (IIIT-D) RGB-D, visual analysis of people (VAP) RGB-D-T, EURECOM, and the authors. In addition, an extended database merged by the first three databases is employed to compare among the proposed method and some existing two-dimensional (2D) and 3D face recognition algorithms. The proposed method possesses satisfactory performance (as high as 99.10 ± 0.52% for Rank 5 recognition rate in their database) with low computation (62 ms for feature extraction) which is desirable for real-time applications.

Inspec keywords: feature extraction; stereo image processing; image colour analysis; image classification; cameras; face recognition; support vector machines

Other keywords: two-dimensional face recognition; depth image; IIIT-D RGB-D database; low-resolution red-green-blue-depth cameras; facial features; facial expression; 3D face recognition; feature dimension; VAP RGB-D-T; MSVM; online classification; RGB-D cameras; Rank 5 recognition rate; multiclass support vector machines; RGB-D face recognition; LBP-RGB-D-MSVM database; time 62.0 ms; local binary pattern; error correcting output codes; feature extraction; EURECOM database

Subjects: Computer vision and image processing techniques; Image recognition; Knowledge engineering techniques

References

    1. 1)
      • 33. Rocha, A., Goldenstein, S.K.: ‘Multiclass from binary: expanding one-versus-all, one-versus-one and ECOC-based approaches’, IEEE Trans. Neural Netw. Learn. Syst., 2014, 25, (2), pp. 289–302.
    2. 2)
      • 26. Ojala, T., Pietikäinen, M., Harwood, D.: ‘A comparative study of texture measures with classification based on feature distributions’, Pattern Recognit., 1996, 29, (1), pp. 51–59.
    3. 3)
      • 30. Hsu, C.-W., Lin, C.-J.: ‘A comparison of methods for multiclass support vector machines’, IEEE Trans. Neural Netw. Learn. Syst., 2002, 13, (2), pp. 415–425.
    4. 4)
      • 16. Abuzneid, M.A., Mahmood, A.: ‘Enhanced human face recognition using LBPH descriptor, multi-KNN, and back-propagation neural network’, IEEE Access, 2018, 6, pp. 20641–20651.
    5. 5)
      • 31. Liu, M., Zhang, D., Chen, S., et al: ‘Joint binary classifier learning for ECOC-based multi-class classification’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (11), pp. 2335–2341.
    6. 6)
      • 24. Itti, L., Koch, C., Niebur, E.: ‘A model of saliency-based visual attention for rapid scene analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (11), pp. 1254–1259.
    7. 7)
      • 21. Azzakhnini, S., Ballihi, L., Aboutajdine, D.: ‘A learned feature descriptor for efficient gender recognition using an RGB-D sensor’. Int. Symp. Signal, Image, Video and Communications (ISIVC), Tunis, Tunisia, 2016, pp. 29–34.
    8. 8)
      • 2. Mudunuri, S.P., Biswas, S.: ‘Low resolution face recognition across variations in pose and illumination’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (5), pp. 1034–1040.
    9. 9)
      • 28. Ross, A., Jain, A.: ‘Information fusion in biometrics’, Pattern Recognit. Lett., 2003, 24, (13), pp. 2115–2125.
    10. 10)
      • 15. Benavides, C., Villegas, J., Román, G., et al: ‘Face classification by local texture analysis through CBIR and SURF points’, IEEE Latin Am. Trans., 2016, 14, (5), pp. 2418–2424.
    11. 11)
      • 14. Vinay, A., Shekhar, V. S., Kumar, C. A., et al: ‘Affine-scale invariant feature transform and two-dimensional principal component analysis: a novel framework for affine and scale invariant face recognition’, IET Comput. Vis., 2016, 10, (1), pp. 43–59.
    12. 12)
      • 12. Ding, C., Choi, J., Tao, D., et al: ‘Multi-directional multi-level dual-cross patterns for robust face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (3), pp. 518–531.
    13. 13)
      • 37. Min, R., Kose, N., Dugelay, J.-L.: ‘KinectFaceDB: a kinect database for face recognition’, IEEE Trans. Syst. Man Cybern. A, Syst., 2014, 44, (11), pp. 1534–1548.
    14. 14)
      • 40. Dautov, R., Distefano, S., Bruneo, D., et al: ‘Data processing in cyber–physical–social systems through edge computing’, IEEE Access, 2018, 6, pp. 29822–29835.
    15. 15)
      • 13. Werghi, N., Tortorici, C., Berretti, S., et al: ‘Boosting 3D LBP-based face recognition by fusing shape and texture descriptors on the mesh’, IEEE Trans. Inf. Forensics Sec., 2016, 11, (5), pp. 964–979.
    16. 16)
      • 18. Hsu, G.-S. J., Liu, Y.-L., Peng, H.-C., et al: ‘RGB-D-based face reconstruction and recognition’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (12), pp. 2110–2118.
    17. 17)
      • 1. Goswami, G., Vatsa, M., Singh, R.: ‘RGB-D face recognition with texture and attribute features’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (10), pp. 1629–1640.
    18. 18)
      • 4. Wang, S.-J., Yan, W.-J., Li, X., et al: ‘Micro-expression recognition using color spaces’, IEEE Trans. Image Process., 2015, 24, (12), pp. 6034–6047.
    19. 19)
      • 36. Nikisins, O., Nasrollahi, K., Greitans, M., et al: ‘RGB-D-T based face recognition’, Int. Conf. Pattern Recognition (ICPR), Stockholm, Sweden, 2014, pp. 1716–1721.
    20. 20)
      • 22. Liu, Z., Wu, M., Cao, W., et al: ‘A facial expression emotion recognition based human–robot interaction system’, IEEE/CAA J. Autom. Sin., 2017, 4, (4), pp. 668–676.
    21. 21)
      • 11. Zhou, Q., Shafiq, U.R., Zhou, Y., et al: ‘Face recognition using dense SIFT feature alignment’, Chin. J. Electron., 2016, 25, (6), pp. 1034–1039.
    22. 22)
      • 8. Bhatt, H.S., Singh, R., Vatsa, M.: ‘On recognizing faces in videos using clustering-based re-ranking and fusion’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (7), pp. 1056–1068.
    23. 23)
      • 5. Ahonen, T., Hadid, A., Pietikäinen, M.: ‘Face description with local binary patterns: application to face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (12), pp. 2037–2041.
    24. 24)
      • 10. Piao, N., Park, R.-H.: ‘Face recognition using dual difference regression classification’, IEEE Signal Process. Lett., 2015, 22, (12), pp. 2455–2458.
    25. 25)
      • 19. Sang, G., Li, J., Zhao, Q.: ‘Pose-invariant face recognition via RGB-D images’, Comput. Intell. Neurosci., 2016, 3563758, pp. 1–9.
    26. 26)
      • 29. He, X., Wang, Z., Jin, C., et al: ‘A simplified multi-class support vector machine with reduced dual optimization’, Pattern Recognit. Lett., 2012, 33, (1), pp. 71–82.
    27. 27)
      • 35. Haykin, S.: ‘Neural networks and learn machines’ (Pearson Edu., Upper Saddle River, NJ, USA, 2009, 3rd edn.).
    28. 28)
      • 6. Baochang, Z., Yongsheng, G., Sanqiang, Z., et al: ‘Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor’, IEEE Trans. Image Process., 2010, 19, (2), pp. 533–544.
    29. 29)
      • 38. Rikhtegar, A., Pooyan, M., Manzuri-Shalmani, M. T.: ‘Genetic algorithm-optimised structure of convolutional neural network for face recognition applications’, IET Comput. Vis., 2016, 10, (6), pp. 559–566.
    30. 30)
      • 27. Ojala, T., Pietikäinen, M., Mӓenpӓӓ, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with LBP’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), p. pp. 971–987.
    31. 31)
      • 23. Hwang, C.-L., Liao, G. H.: ‘Real-time pose imitation by a mid-size humanoid robot with servo-cradle-head RGB-D vision system’, IEEE Trans. Syst. Man Cybern. A, Syst., 2019, 49, (1), pp. 181–191.
    32. 32)
      • 32. Vedaldi, A., Zisserman, A.: ‘Efficient additive kernels via explicit feature maps’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (3), pp. 480–492.
    33. 33)
      • 20. Chen, L., Li, W., Xu, D.: ‘Recognizing RGB images by learning from RGB-D data’. IEEE Conf. Computer Vision and Pattern Recognition, 2014, pp. 1418–1425.
    34. 34)
      • 3. Lajevardi, S.M., Ren, W.H.: ‘Facial expression recognition in perceptual color space’, IEEE Trans. Image Process., 2012, 21, (8), pp. 3721–3733.
    35. 35)
      • 7. Huang, D., Shan, C., Ardabilian, M., et al: ‘Local binary patterns and its application to facial image analysis: a survey’, IEEE Trans. Syst. Man Cybern. C, 2011, 41, (6), pp. 765–781.
    36. 36)
      • 34. Hwang, C.-L., Chen, B.L., Huang, H.H., et al: ‘Hybrid learning model and MMSVM classification for on-line visual imitation of a human with 3-D motions’, Int. J. Robot. Auton. Syst., 2015, 71, pp. 150–165.
    37. 37)
      • 17. Fernández-Cervantes, V., García, A., Ramos, M.A., et al: ‘Facial geometry identification through fuzzy patterns with RGBD sensor’, Computacióny Sistemas, 2015, 19, (3), pp. 529–546.
    38. 38)
      • 25. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA, 2001, vol. 1, pp. 511–518.
    39. 39)
      • 39. Simón, M.O., Corneanu, C., Nasrollahi, K., et al: ‘Improved RGB-D-T based face recognition’, IET Biometrics, 2016, 5, (4), pp. 297–303.
    40. 40)
      • 9. Jianfeng, R., Xudong, J., Junsong, Y.: ‘A chi-squared-transformed subspace of LBP histogram for visual recognition’, IEEE Trans. Image Process., 2015, 24, (6), pp. 1893–1904.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cps.2018.5045
Loading

Related content

content/journals/10.1049/iet-cps.2018.5045
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address