Curating a Benchmark of Image Dataset for Nose and Nostril Detection: Methodology, Diversity, and Implications

Nose and nostril detection plays a crucial role in various computer vision applications, ranging from facial recognition to emotion analysis and healthcare diagnostics. However, the performance of existing nose and nostril detection algorithms is often hindered by the limited availability of high quality and diverse datasets for training and evaluation. To address this limitation, a novel image dataset specifically curated for nose and nostril detection is presented in this paper. The dataset comprises a diverse collection of images capturing variations in positions, lighting conditions, distances, and other relevant factors. The meticulous process of dataset construction, including image selection criteria and the annotation process for labeling the nose and nostril regions have been described. Analysis of the dataset characteristics, including its size, composition, and diversity is included. Application of the curated dataset for nose and nostril detection and performance of various algorithms and approaches have been studied. Additionally, the potential applications and benefits of this dataset, highlighting its capacity to facilitate the development and evaluation of new nose and nostril detection algorithms are discussed. This paper aims to advance the field of nose and nostril detection and inspire future investigations.