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Online ISSN 1751-9640 Print ISSN 1751-9632

IET Computer Vision

Volume 3, Issue 1, March 2009

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Volume 3, Issue 1

March 2009

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    • Iris recognition on edge maps
      • Author(s): N. Sudha ; N.B. Puhan ; H. Xia ; X. Jiang
      • Source: IET Computer Vision, Volume 3, Issue 1, p. 1 –7
      • DOI:  10.1049/iet-cvi:20080015
      • Type: Article
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      • p. 1 –7 (7)
        Iris recognition is a potential tool in secure personal identification and authentication in view of properties such as uniqueness, non-invasiveness and stability of human iris patterns. A new approach based on the Hausdorff distance measure is proposed for iris recognition. In contrast to existing approaches that consider grey or colour images, the new approach considers the binary edge maps of irises. Edge maps have advantages in terms of low storage space, fast transmission, fast processing and hardware compatibility. A new measure, called local partial Hausdorff distance, is computed between the binary edge maps of normalised iris images. The proposed dissimilarity measure has been tested on the high-quality UPOL iris images captured in a constrained environment. The recognition performance of the proposed method has been studied for different values of parameters such as block size and partialness. An appropriate choice of these parameters achieves a recognition rate of more than 98%. The results demonstrate the significance of linear features in the iris edge maps in discriminating different irises.
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      Local probabilistic descriptors for image categorisation
      • Author(s): K. Mele ; D. Suc ; J. Maver
      • Source: IET Computer Vision, Volume 3, Issue 1, p. 8 –23
      • DOI:  10.1049/iet-cvi:20070001
      • Type: Article
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      • p. 8 –23 (16)
        Image categorisation involves the well known difficulties with different visual appearances of a single object, but also introduces the problem of within-category variation. This within-category variation makes highly distinctive local descriptors less appropriate for categorisation. Difficulties because of the within category variation and clutter are tackled by modelling image fragments in a new manner. The authors propose a family of local image descriptors, called probabilistic patch descriptors (PPDs). PPDs encode the appearance of image fragments as well as their variability within a category. PPDs extend the usual local descriptors by also modelling the variance of the descriptors' elements, for example pixels or bins in a histogram. To compare two PPDs, and a PPD with an image, a new similarity measure called PPD matching score is introduced. For each object category, a set of representative PPDs is learnt. Images are represented as feature vectors of the best matching scores obtained for representative PPDs in images. Support vector machine classifiers are then trained on the feature vectors. PPDs are applied to image categorisation using machine learning where the features are the matching scores between images and PPDs. The authors experiment with two variants of PPDs that are based on complementary local descriptors. An interesting observation is that combining the two PPD variants improves the accuracy of categorisation. Experiments indicate that the benefits of modelling the within-category variation give results that are comparable with the state-of-the-art categorisation methods, and show good robustness with respect to noise and occlusions.
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      Automatic skin segmentation and tracking in sign language recognition
      • Author(s): J. Han ; G. Awad ; A. Sutherland
      • Source: IET Computer Vision, Volume 3, Issue 1, p. 24 –35
      • DOI:  10.1049/iet-cvi:20080006
      • Type: Article
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      • p. 24 –35 (12)
        Skin segmentation and tracking play an important role in sign language recognition. A framework for segmenting and tracking skin objects from signing videos is described. It mainly consists of two parts: a skin colour model and a skin object tracking system. The skin colour model is first built based on the combination of support vector machine active learning and region segmentation. Then, the obtained skin colour model is integrated with the motion and position information to perform segmentation and tracking. The tracking system is able to predict occlusions among any of the skin objects using a Kalman filter (KF). Moreover, the skin colour model can be updated with the help of tracking to handle illumination variation. Experimental evaluations using real-world gesture videos and comparison with other existing algorithms demonstrate the effectiveness of the proposed work.
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      Lossless parameterisation of image contour for shape recognition
      • Author(s): W.-S. Lin  and  C.-H. Fang
      • Source: IET Computer Vision, Volume 3, Issue 1, p. 36 –46
      • DOI:  10.1049/iet-cvi:20070042
      • Type: Article
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      • p. 36 –46 (11)
        A parameterised contour that is invariant against affine transformations is a convenient substitution of the object image for shape recognition. Generally, parameterisation needs several representative signals of the image contour to fit the affine transformation model. When the representative signals fail to carry the contour information thoroughly, information loss occurs in the resulting parameterised contour, and so the accuracy of shape recognition may deteriorate. Synthesised feature signals are shown, which represent that an image contour without information loss can be extracted with partial Fourier synthesis or partial cosine synthesis. Lossless parameterisation of the image contour is obtained by substituting the synthesised feature signals into the affine invariant function. Experimental results verify its representative, affine invariance and recognition rate in shape recognition. The results are compared with those by partial wavelet synthesis, which has insignificant information loss.
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