  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 addons limits the
development of academic research in the area. In this paper, we present a
multimodal 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 addons. The dataset
will be publicly available with the acceptance of the research article. The
database is available at: https://github.com/usmancheema89/SejongFaceDatabase.
