## 1 Introduction  
Ear recognition constitutes a prominent area of investigation within the field of pattern recognition [1; 2]; the ultimate objective is to efficiently identify people from images of their ears under uncontrolled conditions. Compared to traditional biometric modalities, the ear has several advantages and is considered one of the most stable anatomical features of the human body. Extensive research has demonstrated the relative stability of ear anatomy, with noteworthy changes primarily occurring before the age of eight and after the age of seventy. This characteristic renders the shape of the ear less susceptible to age-related alterations compared to facial features [3; 4]. Notably, the ear's resilience to modifications induced by cosmetics, hairstyles, expressions, or facial hair provides an intrinsic advantage over facial recognition systems. Moreover, the shape of the ear cannot be obscured by beards, moustaches, or glasses during acquisition. Compared to fingerprint or iris systems, the sensors required for ear biometrics are not specialized or expensive. Additionally, ear image acquisition can be conducted quietly from a distance without the need for user-sensor interaction; this eliminates hygiene concerns and the risk of virus transmission. The public widely accepts the use of ear biometrics in access authentication and security applications, including visa and passport programs [5]. Ear and face features can be used as complementary information in multimodal biometric systems, as both can be captured simultaneously in a single scan. This integration enhances recognition accuracy, frequently in surveillance and tracking applications [6]. For these reasons, more and more research is being conducted into the application of ear recognition in various fields, such as security, surveillance and forensics. Convolutional neural networks (CNNs) have been clearly identified as a reliable data learning technique for solving complex computer vision problems [7; 8; 9]. These developments have facilitated the creation of an end-to-end model that includes simultaneous feature extraction and classification steps [10]. However, despite the acknowledged prowess of CNNs in solving intricate computer vision problems, their application to deep ear recognition systems remains limited. Challenges such as the absence of large public datasets for ear biometrics and the inherent opacity of CNNs as \"black box\" models contribute to this restraint. In addressing the challenges inherent in ear biometric recognition and aiming to enhance performance, this study endeavors to investigate the internal mechanisms of deep learning models. The primary objective is to discern pertinent features within ear images that contribute to effective recognition. To achieve this goal, a two-step deep ear recognition method is proposed, incorporating deep convolutional generative adversarial networks (DCGANs) [11] in the initial stage and mean-class activation maps with convolutional neural networks (Mean-CAM-CNNs) in the subsequent phase. Given the prevalent issue of low visual quality in the utilized datasets, often characterized by darkness or monochromatic attributes, the first step involves training a DCGAN model. This model is designed to colorize grayscale images and enhance dark ear images before their integration into the deep classifier. The second step introduces a novel framework, denominated as Mean-CAM-CNN. This framework capitalizes on class activation maps (CAMs) [12] and incorporates a guided mask to extract a region of interest (RoI) from ear images based on mean heatmaps derived from images of the same class. Such an approach directs the attention of the CNN exclusively to the pertinent information, enhancing the network's focus. The Mean-CAM framework provides high-resolution visualizations with discerning class discriminative power. These pivotal visual representations contribute to a heightened interpretability and transparency of deep ear recognition models.