Geometric Feature Construction and Cluster Analysis of the Concha Region with 2D Images

The auricular concha is a crucial component of the auricle and holds significant importance in areas such as auricular acupuncture point localization in traditional Chinese medicine, auricle recognition, and the design of ear-worn devices. In this study, a segmentation model was first employed to perform eight-region segmentation of auricle images, from which the auricular concha subregion images were extracted. Subsequently, considering the characteristics of ear-worn devices, key points within the auricular concha region were selected and detected using a keypoint detection model. A method for constructing geo-metric features of the auricular concha region was then designed. Finally, three clustering methods-K-means, Deep Clustering, and Autoencoder combined with K-means-were applied to the geometric features of the auricular concha region to generate multiple representative auricular concha templates, followed by comparative analysis. Experimental results demonstrate that the Autoencoder + K-means clustering method achieves superior performance in generating personalized design templates for ear-worn devices. This study provides robust technical support for applications in personalized intelligent wearable device design, auricular acupuncture point localization, and auricle recognition.