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. 2014 Oct 20;14(10):19561-81.
doi: 10.3390/s141019561.

A multi-modal face recognition method using complete local derivative patterns and depth maps

Shouyi Yin  1 , Xu Dai  2 , Peng Ouyang  3 , Leibo Liu  4 , Shaojun Wei  5
Affiliations

A multi-modal face recognition method using complete local derivative patterns and depth maps

Shouyi Yin et al. Sensors (Basel). .

Abstract

In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features.

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Figures

Figure 1.
Figure 1.
The framework of the whole system.
Figure 2.
Figure 2.
The feature decomposition operation.
Figure 3.
Figure 3.
The layering and label coding step.
Figure 4.
Figure 4.
Different situations with the same D value.
Figure 5.
Figure 5.
The process of extracting CLDP-D feature. (a) A depth map displayed as an intensity image; (b) A depth map after preprocessing displayed as an intensity image; (c) 3D face rendered as a smooth shaded surface.
Figure 6.
Figure 6.
The whole framework of our multi-modal 2D + 3D face recognition method.
Figure 7.
Figure 7.
The faces of one subject from fa, fb, fc and dupI subsets (left to right) in turn.
Figure 8.
Figure 8.
The chosen 18 images of person 01 for gallery data.
Figure 9.
Figure 9.
Results of changing the weights of CLDP-G and CLDP-D.
Figure 10.
Figure 10.
The ROC curves of corresponding methods on the three subsets.
Figure 11.
Figure 11.
Sample of testing images in the Curtin-PE subset.
Figure 12.
Figure 12.
Sample of testing images in the Curtin-IE subset.
Figure 13.
Figure 13.
Sample of testing images in the Curtin-D subset.
Figure 14.
Figure 14.
Four 2D images (left to right: FALM, FBLM, FALF, and FBLF) and one 3D image (rightmost) of a person acquired in each session.
Figure 15.
Figure 15.
The experimental results on the Notre-Dame dataset.
See this image and copyright information in PMC

References

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