Abstract
In this work, we present a robust face authentication approach merging multiple descriptors and exploiting both 3D and 2D information. First, we correct the heads rotation in 3D by iterative closest point algorithm, followed by an efficient preprocessing phase. Then, we extract different features namely: multi-scale local binary patterns (MSLBP), novel statistical local features (SLF), Gabor wavelets, and scale invariant feature transform (SIFT). The principal component analysis followed by enhanced fisher linear discriminant model is used for dimensionality reduction and classification. Finally, fusion at the score level is carried out using two-class support vector machines. Extensive experiments are conducted on the CASIA 3D faces database. The evaluation of individual descriptors clearly showed the superiority of the proposed SLF features. In addition, applying the (\(\hbox {3D} + \hbox {2D}\)) multimodal score level fusion, the best result is obtained by combining the SLF with the \(\hbox {MSLBP}+\hbox {SIFT}\) descriptor yielding in an equal error rate of 0.98 % and a recognition rate of \(\hbox {RR} = 97.22\,\%\).
Similar content being viewed by others
References
Hjelmas, E., Low, B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83, 236–274 (2001)
Eskandari, M., Toygar, O.: Fusion of face and iris biometrics using local and global feature extraction methods. Signal Image Video Process. 8(6), 995–1006 (2014)
Buciu, I., Kotropoulos, C., Pitas, I.: Comparison of ICA approaches for facial expression recognition. Signal Image Video Process. 3(4), 345–361 (2009)
Sao, A.K., Yegnanarayana, B.: On the use of phase of the Fourier transform for face recognition under variations in illumination. Signal Image Video Process. 4(3), 353–358 (2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Lajevardi, S.M., Hussain, Z.M.: Automatic facial expression recognition: feature extraction and selection. Signal Image Video Process. 6(1), 159–169 (2012)
Hadid, Abdenour, Dugelay, Jean-Luc, Pietikinen, Matti: On the use of dynamic features in face biometrics: recent advances and challenges. Signal Image Video Process. 5(4), 495–506 (2011)
Lu, J., Plataniotis, K., Venetsanopoulos, A.: Face recognition using LDA-based algorithms. IEEE Trans. Neural Netw. 14(1), 195–200 (2003)
Yurtkan, K., Demirel, H.: Entropy-based feature selection for improved 3D facial expression recognition. Signal Image Video Process. 8(2), 267–277 (2014)
Ming, Y.: Rigid-area orthogonal spectral regression for efficient 3D face recognition. Neurocomputing 129(10), 445–457 (2014)
Ouamane, A., Belahcene, M., Benakcha, A., Boumehrez, M., Ahmed, A.T.: The classification of scores from multi-classifiers for face verification. Sens. Transducers J. 145(10), 116–118 (2012)
Chen, Y., Medioni, G.: Object modeling by registration of multiple range images. In: Robotics and Automation, pp. 2724–2729
Huang, D., Ouji, K., Ardabilian, M., Wang, Y., Chen, L.: 3D Face recognition based on local shape patterns and sparse representation classifier. Lect. Notes Comput. Sci. 6523, 206–216 (2011)
Xu, C., Li, S., Tan, T., Quan, L.: Automatic 3D face recognition from depth and intensity Gabor features. Pattern Recognit. 42(9), 1895–1905 (2009)
Lowe, D.: Demo Software: SIFT Keypoint Detector. http://www.cs.ubc.edu.ca/lowe/, 2006
Xu, C., Wang, Y., Tan, T., Quan, L.: 3D Face recognition based on G-H shape variation. Lect. Notes Comput. Sci. 3338, 233–243 (2005)
Wang, X., Ruan, Q. Ming, Y.: 3D Face recognition using corresponding point direction measure and depth local features. In: IEEE 10th International Conference on Signal Processing (ICSP), pp. 86–89 (2010)
Ming, Y., Ruan, Q.: Robust sparse bounding sphere for 3D face recognition. Image Vis. Comput. 30(8), 524–534 (2012)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ouamane, A., Belahcene, M., Benakcha, A. et al. Robust multimodal 2D and 3D face authentication using local feature fusion. SIViP 10, 129–137 (2016). https://doi.org/10.1007/s11760-014-0712-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-014-0712-x