Face Recognition Image Dataset

Currently, face recognition is widely used in smart devices, but factors such as environmental changes and lighting effects pose challenges to recognition accuracy. Existing datasets often lack diversity and annotation quality, limiting the algorithm's performance improvement. This dataset aims to improve the accuracy of recognition algorithms by providing a rich diversity of high-quality face images. During data collection, various types of camera equipment were used to shoot under different lighting and environmental conditions to ensure data diversity. Multiple rounds of annotation and consistency checks were utilized to ensure annotation accuracy, with the annotation team comprising experienced professionals. Data preprocessing includes steps such as denoising, alignment, and normalization. Data is organized in JPG format for easy storage and access. The core advantage of this dataset is its high-quality data annotation, with annotation accuracy exceeding 98%, and the adoption of new data augmentation techniques such as random cropping and rotation, which improve model robustness. Compared to similar datasets, this dataset holds advantages in diversity and annotation consistency. The provided high-precision data significantly improves the performance of face recognition models in low-light environments, with a 15% increase in recognition rate. Additionally, the dataset covers various skin tones, ages, and genders, offering high rarity and broad generalizability suitable for various face recognition application scenarios.