# Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine
## 5 Conclusions  
In this paper, 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 Machine (CERMB-SVM) algorithm, which significantly outperforms the statistical based ear recognition techniques and gives superior results to those of the learning-based methods in terms of accuracy. The proposed CERMB-SVM method splits the input ear image into several bands based on the intensity of its pixels. Canny edge detection algorithm along with morphological operators were used to generate and select the edge map for each resulting bands. One binary edge map image was created by combining the edge maps of different image bands. This resulting single binary edge map was divided into cells. The Freeman chain code for each cell was then calculated. The resulting cells are then clustered into overlapping blocks, and a histogram for each block is computed. The resulting histograms are normalized and linked together to create a normalized chainlet histogram vector for the original input image. The normalized chainlet histogram vectors of the images of the dataset are considered as features for matching using pairwise SVM. Experimental results show that the proposed CERMB-SVM technique significantly outperforms the statistical-based techniques, in terms of accuracy. The proposed CERMB-SVM technique generates 62.67%, 9.24%, 7.90% and 0.48% higher than single image PCA, Eigenfaces, 2D-MBPCA and anchor chainlet in terms of accuracy on images of ITID II dataset, respectively. The proposed algorithm produces 54.44%, 23.51%, 14.25% and 0.42% higher accuracy than single image PCA, Eigenfaces, 2D-MBPCA and anchor chainlet \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{2-3}  & **IITD II** & **USTB I** \\ \hline \multicolumn{3}{c}{_PCA-based Techniques_} \\ \hline Single Image PCA & 13.55 & 12.16 \\ Eigenfaces [4] & 3.10 & 1.82 \\
2D-MBPCA [17] & 13.64 & 13.07 \\ \hline \multicolumn{3}{c}{_Learning based Techniques_} \\ \hline BSIF and SVM [15] & 23.57 & - \\ GoogLeNet [11] & 22.88 & 21.59 \\ ResNet18 and SVM [12] & 24.24 & 23.88 \\ VGG-based Ensembles [23] & 23.51 & 22.79 \\ Neural Network and SVM [14] & - & 22.78 \\ \hline \multicolumn{3}{c}{**Proposed CERMB-SVM Technique**} & **22.21** & **21.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average execution time (milliseconds) of the suggested CERMB-SVM and the state-of-the-art PCA-based and learning-based methods. technique on images of USTM I dataset, respectively. Experimental results show that the proposed CERMB-SVM technique generates superior or the same performance in terms of accuracy than those of learning-based methods. It generates 1.71%, 0.45%, 0.26% and 0.14% higher accuracy on images of IITD II datasets compared to those of \"BSIF and SVM\", \"GoogLeNet2\", \"ResNet18 and SVM\" and \"Neural Network and SVM\" techniques, respectively. Moreover, the proposed technique produces 0.08%, 0.2%, 1.14% greater accuracy on images of USTM I dataset than those of \"GoogLeNet\", \"VGG-based Ensembles\" and \"Neural Network and SVM\" methods, respectively. The proposed algorithm generates similar results to \"ResNet18 and SVM\" method. The proposed CERMB-SVM algorithm can be applied to different applications including iris and drone recognition. The MATLAB implementation of the algorithm indicates that the proposed algorithm generates competitive results compared to those of learning-based algorithms at a portion of their computation cost; however, the real-time implementation of the proposed algorithm on DSP or FPGA can be considered as the future work for this research. Conceptualization, M.M.Z. and A.S.-A. ; Funding acquisition, A.S.-A. and D.M. ; Investigation, M.M.Z. ; Methodology, M.M.Z. and A.S.-A. ; Software, M.M.Z. ; Supervision, A.S.-A. and D.M. ; Writing--original draft, M.M.Z.