Periocular Recognition Facial Recognition wearing Masks
In general, face recognition requires certain conditions such as fully frontal face images as well as consistent lighting. Furthermore, facial recognition is not applicable for healthcare professionals wearing surgical masks. Due to COVID 19, everyday life is now dominated by such face masks, as well. To overcome these challenges, BioID has developed a patented periocular recognition, which recognizes a person through the area around their eyes. This means the technology is less exclusive, broadens the user population and the number of use cases. Additionally, a person who is enrolled with their eyes can be readily recognized whether or not their face is partially obscured, e.g. wearing medical masks (e.g. due to Coronavirus).
Facial Recognition with Medical Masks
AUTHENTICATION With Face MASKS
Alongside the web-based face recognition available in the BioID Web Service BWS, the periocular trait gives developers an additional strong choice when integrating biometric authentication with their applications. Eye recognition enables new use cases that were not possible with face recognition alone. As this technology focuses on a small part of the face which is less sensitive to lighting conditions and is more precise in terms of facial characteristics, it is accurate, flexible and accepted in any situation.
Broadening APPLICATIONS For COVID 19 SCEnarios
Just like face recognition, BioID’s periocular recognition works with a standard smartphone camera, webcam or security camera and requires only a simple selfie. It looks at the fine features around the eyes, such as lashes, lid, brow, and folds in the skin, as well as the full or partial iris (the patterns in the colored part of the eye) when visible. Since only the eyes are needed, it is an ideal solution for applications where the mouth and nose may be covered by a cleanroom or medical mask, as it is the standard in times of the SARS-CoV-2 pandemic.
Two biometrics (face & eye) is better than one
Because BioID’s periocular recognition is less sensitive than face recognition to varied lighting conditions (thanks in part to a unique patented feature extraction process), it works well for applications with challenging lighting. Furthermore, the technology is also more robust to appearance changes such as facial hair or makeup. Eye recognition can always be combined with face recognition for increased accuracy, thus providing greater performance with the same intuitive user experience. Learn more at the Developer Documentation.