## 2 Related Work
### Deep Learning Methods  
Recently, there has been a notable shift in the emphasis of ear biometrics from conventional feature engineering approaches to those rooted in deep learning paradigms [19], [PERSON] et al. [20] conducted a comparative analysis between handcrafted and CNN-based ear recognition models. Their evaluation, focused on unconstrained ear recognition, involved seven well-established handcrafted descriptors and four CNN models based on AlexNet variants. The CNNs exhibited superior recognition accuracy compared to handcrafted features. Subsequently, [PERSON] et al. [21] suggested an innovative ear identification system predicated on sets of deep CNN models. Notably, CNNs were trained on visual geometry group (VGG)-based network topologies to extract deep features. Through the testing of various VGG representations, the VGG-13-16-19-set configuration emerged as the optimal choice. [PERSON] et al. [22] contributed a six-layer deep CNN tailored for ear identification. Their exploration involved the systematic testing of activation functions, learning rates, kernel sizes, and epochs to assess the proposed model. CNNs demonstrated the capability to directly learn optimal features from input images, surpassing the performance of traditional descriptors. [PERSON] and [PERSON] [23] addressed the challenge of test images lacking color information for a system trained on color images. They em ployed a DCGAN model to colorize and enhance grayscale and dark images, subsequently classifying ears using a CNN-based classification model, with observed improvements in recognition performance. In a recent investigation, [PERSON] et al. [24] developed and combined several ensembles based on ResNet architectures with varying depths to enhance recognition performance. They also explored the application of the support vector machine (SVM) classifier as a substitute to the classical dense classifier. [PERSON] et al. [25] presented a novel ear identification method grounded in Mahalanobis distance learning, utilizing deep CNN features extracted from VGG and ResNet models. [PERSON] [26] proposed a two-stage ear recognition approach, where the discrete Curvelet transform extracted key ear features, and subsequently, end-to-end deep learning network ensembles were deployed for classification. [PERSON] et al. [27] proposed human ear recognition approach based on channel features and dynamic convolution (CFDCNet). CFDCNet adapts the DenseNet-121 model for ear feature extraction using dynamic convolution, enhancing feature aggregation within class samples and dispersion across different samples. Additionally, a channel attention mechanism prioritizes important ear features while suppressing irrelevant ones, boosting the discriminative capability of the feature representation. [PERSON] et al. [28] introduced a novel unsupervised lightweight neural network architecture focused on ear print recognition. This architecture comprises a singular layer and is named MDFNet due to its reliance on gradient magnitude, gradient direction, and the responses generated by data-driven filters.  
The deployment of CNNs in ear recognition models has been notably limited, primarily attributed to the lack of challenging and extensive databases. This paper introduces a novel approach, leveraging a DCGAN in conjunction with Mean-CAM-CNN, as an alternative solution. The aim of this proposal is to address the limitations of current deep ear recognition models systematically, while also improving the effectiveness of classical CNNs.