# Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine
## 1 Introduction  
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. Ears are distinctive to an individual; even indistinguishable twins' ear patterns are different [1]. There are several challenges attached with ear recognition in comparison to face recognition. More obstruction can be found when dealing with ear images than face images due to coverings that are sometimes present in such images, e.g., hair and jewelry. Additionally, there is currently a limited number of ear image datasets available. These datasets usually contain a small number of images. A typical ear recognition technique consists of a feature extractor and a classification method. Some of the existing feature extraction algorithms for ear recognition are Principal Component Analysis (PCA) [2, 3, 4, 5, 6], Curvelet-based [7], wavelet-based [8], local oriented patterns based [9] and neural-network-based techniques [10, 11, 12, 13]. Over the years, researchers have proposed several machine-learning-based approaches and statistical methods for ear recognition. Some of these methods include: 'Eigenfaces' [6], wavelet [8], SVM [14, 15] and deep learning [12] based techniques for feature extraction and classification. Both learning and statistical-based algorithms have been successfully used for ear recognition; however, more accurate results are often obtained using the learning-based techniques due to the 'width' of the data. Promising results have been noticed with recently reported statistical-based algorithms, e.g., chainlets [16] and 2D-MBPCA [17]. High-performing ear recognition techniques use a mixture of statistical-based feature extraction methods along with a learning-based classification algorithm [2]. This hasinspired the authors to investigate a new combination of multi-band image processing with chainlets and a learning-based classifier. Application of the multi-band image processing for ear recognition on non-decimated wavelet subbands of ear images utilizing Principal Component Analysis (PCA) shows the effectiveness of multi-band image processing in recognition. In [18], the authors showed that the intersection of the number of feature graphs and the Eigenvector energy defines the optimum number of bands for recognition, where increasing the number of multi-band images changes the distribution of the energy across image Eigenvectors, and consolidates most of the image Eigenvectors' energy into a smaller number of Eigenvectors. The result of this was an increased accuracy in recognition. 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; however, to the authors' knowledge, the application of multi-band image processing along with chainlets for ear recognition has not been informed in the literature. This has motivated the authors to investigate a new combination of multi-band image processing with chainlets and a learning-based classifier for ear recognition. This paper presents a Chainlet-based Multi-Band Ear Recognition method using Support Vector Machine (CERMB-SVM). The suggested algorithm splits the input ear image into a few image bands based on the image pixel intensity. More ear features can be extracted using the created image bands rather than just the input image. The Canny edge detection is performed on each generated image band, generating a binary image representing its edges. To remove isolated edges, connect adjacent left edges and discard inappropriate edges within each resulting binary image, morphological operators are employed. The generated edge bands are then compacted into one binary edge map.