Abstract
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.
Highlights
Ear recognition is a biometric identification method in which an ear image is utilized to distinguish a person, which has advanced over the past years
Statistical-based methods, comprising Principal Component Analysis (PCA), eigenfaces, 2D-MBPCA and the anchor chainlet technique, extract some statistics or features directly from the image and use these features to find the best match, whereas learningbased methods employ a range of information comprising image statistics, features and other data extracted from the image dataset to train classifiers, e.g., neural networks and support vector machines such as the proposed CERMB-Support Vector Machine (SVM) method
Application of multi-band image processing together with chainlets and support vector machine for ear recognition was investigated. This resulted in development of a Chainlet-based Ear Recognition Algorithm using Multi-Banding and Support Vector
Summary
Ear recognition is a biometric identification method in which an ear image is utilized to distinguish a person, which has advanced over the past years. High-performing ear recognition techniques use a mixture of statistical-based feature extraction methods along with a learning-based classification algorithm [2] This has inspired the authors to investigate a new combination of multi-band image processing with chainlets and a learning-based classifier. In [16], the authors introduced chainlets as an efficient feature descriptor for encoding the shapes formed by the edges of an object, where the connections and orientations of the edges are more invariant to translation and rotation They have successfully applied their method to ear recognition and reported promising results; to the authors’ knowledge, the application of multi-band image processing along with chainlets for ear recognition has not been informed in the literature. The rest of the paper is structured as follows: Section 2 presents the proposed CERMB-SVM algorithm, Section 3 discusses the pairwise support vector machine classifier, Section 4 presents the experimental results, and Section 5 concludes the paper
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