# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 1 Introduction  
Recent years have witnessed the increased application of biometric identification systems across various fields, thus necessitating their improvements in terms of accuracy and usability. To develop identification systems, various biometric traits, including fingerprint- [1, 2, 3], finger-vein- [2, 4, 5, 6], face- [7, 8, 9, 10, 11, 12, 13, 14, 15, 16], palm-vein- [17], palm-print- [18, 19, 20], retina- [21, 22], sclera- [23], voice- [24, 25], and ear-based [26, 27, 28, 29] recognitions have been deployed. These modalities constitute fundamental components in the evolution of biometric systems [30], with six key criteria that must be satisfied [26]:  
1. Universality: each individual should have the trait;
2. Distinctiveness: able to distinguish between different individuals;
3. Permanence: has sufficient invariance over age;Performance: achieves the required recognition accuracy and speed, as well as being robust against operational and environmental factors affecting them;
* Collectability: can be acquired and measured easily and quantitatively;
* Acceptability: the extent individuals accept these biometric characteristic technologies. However, no single biometric trait has been able to perfectly satisfy all six criteria. Therefore, multimodal biometrics, which integrates multiple modalities to compensate for the limitations of individual modalities, have been explored. The ear is a non-contact, long-range biometric modality that is generally less affected by aging compared with other modalities, such as fingerprints. It is among the few biometric traits that satisfy the permanence criterion owing to its aging-tolerant morphology [26]. Furthermore, the extant studies revealed that even various individuals, including genetically identical twins, exhibit distinct ear morphologies [31]. These characteristics render the ear a promising candidate for contactless, long-range identification systems. Therefore, a variety of ear-based biometric identification methods has been explored. For example, [PERSON] et al. employed local binary patterns (LBPs) for ear-image-based recognition, achieving a maximum identification accuracy of 0.986 [26]. However, their experiment was exclusively based on the frontal images of the auric, thereby disregarding image-orientation variations. Consequently, their identification accuracy decreased dramatically to 0.387 when rotated ear images were introduced. To address this limitation, [PERSON] et al. applied a convolutional neural network (CNN) and bidirectional long short-term memory (LSTM) on the same dataset. Their results indicated improved accuracy (0.928-0.943) [29]. Similarly, [PERSON] et al. performed CNN-based identification using a dataset containing rotated ear images, achieving identification accuracies of 0.910-0.920 [28]. The reviewed studies indicated that CNN-based models exhibit significant robustness to orientation variations in ear images. However, their effectiveness in real-world applications is still limited by the limited orientation diversity in the training datasets. Moreover, the practical deployment of ear-based biometric identification requires the ability to recognize individuals from multiple directions. This is particularly relevant in applications, such as multimodal biometrics and surveillance-based identification systems. Multimodal biometrics typically comprises the acquisition of ear images from the same direction as the face. However, surveillance-based applications comprise the capturing of images from behind or at other oblique angles. Despite these application potentials, the extant studies have barely considered the systematic evaluation of the feasibility of ear-based identification using datasets incorporating multiple viewpoints. This research gap necessitates further studies on the effectiveness of ear-based identification using images captured from diverse angles. To address the aforementioned research gap, we tested the following two hypotheses:  
1. Identification based on ear images captured from multiple directions is feasible.