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Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine

Zarachoff, Matthew Martin and Sheikh-Akbari, Akbar and Monekosso, Dorothy (2022) 'Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine.', Applied sciences., 12 (4). p. 2033.


This paper introduces the Chainlet-based Ear Recognition algorithm using Multi-Banding and Support Vector Machine (CERMB-SVM). The proposed technique splits the gray input image into several bands based on the intensity of its pixels, similar to a hyperspectral image. It performs Canny edge detection on each generated normalized band, extracting edges that correspond to the ear shape in each band. The generated binary edge maps are then combined, creating a single binary edge map. The resulting edge map is then divided into non-overlapping cells and the Freeman chain code for each group of connected edges within each cell is determined. A histogram of each group of contiguous four cells is computed, and the generated histograms are normalized and linked together to create a chainlet for the input image. The created chainlet histogram vectors of the images of the dataset are then utilized for the training and testing of a pairwise Support Vector Machine (SVM). Results obtained using the two benchmark ear image datasets demonstrate that the suggested CERMB-SVM method generates considerably higher performance in terms of accuracy than the principal component analysis based techniques. Furthermore, the proposed CERMB-SVM method yields greater performance in comparison to its anchor chainlet technique and state-of-the-art learning-based ear recognition techniques.

Item Type:Article
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Available under License - Creative Commons Attribution 4.0.
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Publisher statement:This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Date accepted:08 February 2022
Date deposited:06 July 2022
Date of first online publication:16 February 2022
Date first made open access:06 July 2022

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