# Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine
## 4 Experimental Results  
Two standard benchmark ear image datasets named the Indian Institute of Technology Delhi II (IITD II) [19] and the University of Science and Technology Beijing I (USTB I) [20], which are widely used in the literature [8; 14; 15; 17; 22], were used to create experimental results. These datasets were chosen since their images are aligned and also, they have extensively used in recent journal and conference papers. The IITD II dataset contains 793 images of the right ear of 221 people, where there are three to six images for each person's ear and the images are unsigned 8-bit grayscale of size 180 \(\times\) 50 pixels. All the images of IITD II dataset have the same size and are centered and aligned manually. The USTB I dataset contains 180 unsigned 8-bit grayscale of size 150 \(\times\) 80 images of the right ear of 60 people (three images per person). The images in USTB I are tightly cropped; however, they demonstrate some minor rotation and shearing. Example images from both datasets are shown in Figure 4.  
The suggested CERMB-SVM algorithm was applied to images of the IITD II and USTB I datasets utilizing two to ten bands of constant size as detailed in Section 2.2. The number of accurate matches was computed for each set of bands. A subgroup of the results for both the IITD II and USTB I image datasets are presented in Tables 1 and 2 respectively. From these tables, it is clear that the proposed technique attains its highest performance at four and seven bands when dealing with the images of the IITD II and USTB I datasets, respectively. From these tables, it can be observed that the proposed CERMB-SVM technique has marginally superior performance when dealing with the images of the IITD II dataset than the images of the USTB I dataset. To assess and compare the effectiveness of the suggested CERMB-SVM algorithm with the statistical PCA and anchor chainlet, and state-of-the-art learning based methods, the Rank-1 experimental results of the suggested CERMB-SVM, single image PCA, 'Eigenfaces' [4], 2D-MBPCA [17], 'BSIF and SVM' [15], GoogLeNet [11], 'ResNet18 and SVM' [12], VGG-based Ensembles [23] and 'neural network and SVM' based [14] methods are presented in Table 3. From this table, the presented CERMB-SVM method meaningfully outperforms both the PCA-based and learning based state-of-the-art algorithms for the images of the IITD II dataset. Additionally, the proposed CERMB-SVM method considerably outperforms the PCA-based methods and slightly outperforms the learning-based algorithms on the USTB I dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Bands** & **Rank-1** & **Rank-5** \\ \hline
2 & 97.79 & 99.85 \\
3 & 98.44 & 100.00 \\
4 & **99.02** & **100.00** \\
5 & 98.25 & 100.00 \\
6 & 96.94 & 99.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results for the suggested Chainlet-based Multi-Band Ear Recognition using Support Vector Machine (CERMB-SVM) ear recognition algorithms on the IITD II dataset. Figure 4: Two example images of unique persons from the IITD II dataset (**a,b**) [19]. Two example images of two unique persons from the USTB I dataset (**c,d**) [20]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Bands** & **Rank-1** & **Rank-5** \\ \hline
5 & 99.17 & 100.00 \\
6 & 99.17 & 100.00 \\
7 & **99.44** & **100.00** \\
8 & 99.44 & 100.00 \\
9 & 98.89 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results for the suggested Chainlet-based Multi-Band Ear Recognition using Support Vector Machine (CERMB-SVM) ear recognition algorithm on the USTB I [20] dataset. A further comparison between the proposed 2D-CERMB-SVM technique and the aforementioned algorithms is demonstrated using Cumulative Match Curves (CMC). Regions of interest for the CMC curves comparing 2D-CERMB-SVM to the statistical based methods on the IITD II and USTB I datasets are shown in Figures 5 and 6, respectively.