Sejong face database: A multi-modal disguise face database

Commercial application of facial recognition demands robustness to a variety of challenges such as illumination, occlusion, spoofing, disguise, etc. Disguised face recognition is one of the emerging issues for access control systems, such as security checkpoints at the borders. However, the lack of availability of face databases with a variety of disguise add-ons limits the development of academic research in the area. In this paper, we present a multi-modal disguised face dataset to facilitate the disguised face recognition research. The presented database contains 8 facial add-ons and 7 additional combinations of these add-ons to create a variety of disguised face images. Each facial image is captured in visible, visible plus infrared, infrared, and thermal spectra. Specifically, the database contains 100 subjects divided into Subset-A (30 subjects, 1 image per modality) and Subset-B (70 subjects, 5 plus images per modality). We also present baseline face detection results performed on the proposed database to provide reference results and compare the performance in different modalities. Qualitative and quantitative analysis is performed to evaluate the challenging nature of disguise add-ons. The dataset will be publicly available with the acceptance of the research article.

Automated summary

This paper introduces a multi-modal disguised face dataset to facilitate research in disguised face recognition and performs qualitative and quantitative analysis to evaluate the challenging nature of disguise add-ons. The dataset will be publicly available.