New Benchmarks for Asian Facial Recognition Tasks: Face Classification
  with Large Foundation Models
Jinwoo Seo Soora Choi Eungyeom Ha Beomjune Kim Dongbin Na
The face classification system is an important tool for recognizing personal
identity properly. This paper introduces a new Large-Scale Korean Influencer
Dataset named KoIn. Our presented dataset contains many real-world photos of
Korean celebrities in various environments that might contain stage lighting,
backup dancers, and background objects. These various images can be useful for
training classification models classifying K-influencers. Most of the images in
our proposed dataset have been collected from social network services (SNS)
such as Instagram. Our dataset, KoIn, contains over 100,000 K-influencer photos
from over 100 Korean celebrity classes. Moreover, our dataset provides
additional hard case samples such as images including human faces with masks
and hats. We note that the hard case samples are greatly useful in evaluating
the robustness of the classification systems. We have extensively conducted
several experiments utilizing various classification models to validate the
effectiveness of our proposed dataset. Specifically, we demonstrate that recent
state-of-the-art (SOTA) foundation architectures show decent classification
performance when trained on our proposed dataset. In this paper, we also
analyze the robustness performance against hard case samples of large-scale
foundation models when we fine-tune the foundation models on the normal cases
of the proposed dataset, KoIn. Our presented dataset and codes will be publicly
available at https://github.com/dukong1/KoIn_Benchmark_Dataset.