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. 2014:2014:879031.
doi: 10.1155/2014/879031. Epub 2014 Aug 6.

A modified active appearance model based on an adaptive artificial bee colony

Mohammed Hasan Abdulameer  1 , Siti Norul Huda Sheikh Abdullah  2 , Zulaiha Ali Othman  3
Affiliations

A modified active appearance model based on an adaptive artificial bee colony

Mohammed Hasan Abdulameer et al. ScientificWorldJournal. 2014.

Abstract

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

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Figures

Figure 1
Figure 1
Adaptive ABC algorithm.
Figure 2
Figure 2
Adaptive and conventional ABC algorithms performance in terms of their nectar values.
Figure 3
Figure 3
Sample images from (a) CASIA face dataset, (b) 2.5D face dataset, and (c) UBIRIS iris dataset.
Figure 4
Figure 4
Average of ten cross validation results recognition performance for three datasets in terms of (a) accuracy, (b) sensitivity, and (c) specificity for conventional AAM, adaptive AAM, and the proposed AAM.
Figure 5
Figure 5
The average time taken to fit the model in the original image by conventional and adaptive ABC over the three datasets.
Figure 6
Figure 6
Standard deviation taken by conventional and adaptive ABC algorithm for the three datasets.
Figure 7
Figure 7
Fitting model example of faces at (a) iteration 1, (b) iteration 2, and (c) iteration 3, (d) final fit model, (e) standard AAM image, and (f) original image.
Figure 8
Figure 8
Graphical representation of the best food sources with number of iterations.
Figure 9
Figure 9
Fitting error average for 10 rounds taken from fitting the model in the original image by conventional AAM, adaptive AAM, and the proposed AAM.
Algorithm 1
Algorithm 1
Algorithm 2
Algorithm 2
See this image and copyright information in PMC

References

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