## 5 Conclusions  
This paper presents a practical approach leveraging the combination of DCGAN and Mean-CAM-CNN for addressing the intricate challenge of ear biometric recognition. The recognition process encompasses two primary phases. In the initial stage, a trained DCGAN model is deployed to colorize and enhance ear images. Subsequently, a Mean-CAM-CNN framework is introduced to extract salient features and execute efficient classification. Specifically, the Mean-CAM method is employed to delineate a region of interest in the image based on mean heatmaps derived from images of the same class. Following this, a CNN is trained solely using the extracted regions for the purpose of classifying and recognizing ear images. Comprehensive experiments were conducted on unconstrained ear recognition databases, namely, MAI and AWE, to substantiate the efficacy of the proposed approach. The optimal Rank-1 recognition rates achieved were 100.00% and 76.25% for MAI and AWE, respectively. In particular, Mean-CAM was observed to enhance the Rank-1 recognition rates by 1.01% for the MAI dataset and 16.75% for the AWE dataset, whereas DCGAN was found to elevate the Rank-1 recognition rates by 0.33% for the MAI dataset and 1.75% for the AWE dataset. The added value of the proposed framework is justified by the superior learning capabilities inherent in CNNs, the efficacy of the employed preprocessing technique, the elimination of extraneous information, and the selective extraction of information pertinent to recognition through the utilization of the Mean-CAM cropping strategy. In forthcoming endeavors, our objective is to expand upon our research to enhance its efficacy in accurately identifying individuals from ear images captured under uncontrolled conditions. This will be achieved through an exploration of several key areas:  
* Firstly, we aim to investigate various feature visualization techniques, such as t-distributed stochastic neighbor embedding (t-SNE). This analysis will allow us to gain deeper insights into the discriminatory features inherent in ear images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Publication** & **Year** & **Method** & **MAI** & **AWE** \\ \hline \multirow{8}{*}{**Handcrafted**} & [PERSON] et al. [13] & 2019 & LBP variants & 73.71 & 49.60 \\ \cline{2-5}  & [PERSON] et al. [14] & 2020 & RLOP & 72.29 & 54.10 \\ \cline{2-5}  & [PERSON] et al. [15] & 2020 & [PERSON] algorithm + SURF & / & 44.00 \\ \cline{2-5}  & [PERSON] and [PERSON] [16] & 2020 & GZ + LPQ & / & 53.50 \\ \cline{2-5}  & [PERSON] and [PERSON] [17] & 2020 & BSIF & / & 44.53 \\ \cline{2-5}  & [PERSON] et al. [18] & 2022 & 1D multi-resolution LBP & 100.00 & 43.00 \\ \hline \multirow{8}{*}{**Deep-Learning**} & [PERSON] et al. [20] & 2019 & VGG-13-16-19-ensembles & 93.96 & / \\ \cline{2-5}  & [PERSON] et al. [21] & 2019 & AlexNet (Fine-tuned) & 94.50 & / \\ \cline{2-5}  & [PERSON] et al. [22] & 2020 & CNN & 96.99 & / \\ \cline{2-5}  & [PERSON] and [PERSON] [23] & 2021 & DCGAN + VGG16 & 96.00 & 50.53 \\ \cline{2-5}  & [PERSON] et al. [24] & 2021 & Combination of ResNet features & 99.64 & 67.25 \\ \cline{2-5}  & [PERSON] et al. [25] & 2021 & Mahalanobis distance + CNN & 97.80 & / \\ \cline{2-5}  & [PERSON] [26] & 2022 & Discrete curvelet transform + Ensemble of ResNet features & 99.45 & / \\ \cline{2-5}  & [PERSON] et al. [27] & 2023 & CFDCNet & 99.70 & 72.70 \\ \hline [PERSON] et al. [28] & 2023 & MDFNet & 97.67 & / \\ \cline{2-5}  & **Our proposed method** & **2024** & **DCGAN + Mean-CAM-CNN** & **100.00** & **76.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of Rank-1 recognition rates: evaluating the DCGAN + Mean-CAM-CNN approach against competing methods on MAI and AWE datasets. Secondly, we plan to evaluate multiple CNN architectures concurrently. This comparative study will enable us to identify the most effective CNN architecture for our specific task, thereby optimizing the performance of our system.