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These updates will provide customers with an enhanced ability to detect more faces from the images (even the difficult ones), perform more accurate face matches, as well as obtain the improved age, gender, and emotion attributes for the faces in images.
Amazon Rekognition can now detect 40% more faces, and the face recognition feature produces 30% more correct best matches. The rate of false detections has also dropped down by 50%.
Additionally, face matches now have more consistent similarity scores that vary across lighting, pose, and appearance, letting the customers use higher confidence thresholds, avoid false matches and reduce human review in identity verification applications. Face detection algorithms usually suffer difficulty when it comes to detecting faces in images with challenging aspects.
These challenging aspects include pose variations (caused by head movement or camera movements, difficult lighting (low contrast and shadows, washed out faces), and a blur or occlusion (faces covered by hat, hair, or hands). Pose variation issue is generally encountered in faces that have been captured from acute camera angles (shots taken from above or below a face), shots with a side-on view of a face, or when the subject is looking away. This particular issue is typically seen in social media photos, selfies, or fashion photoshoots.
Lighting issue is common in stock photography and at event venues where there isn’t enough contrast between facial features and the background in low lighting. Occlusion is seen in photos with artistic effects (selfies or fashion photos, video motion blur), fashion photography or photos taken from identity documents. With the latest update, Rekognition has become very efficient at handling all the different aspects of challenging images that have been captured in unconstrained environments, announces AWS.
For more information, check out the official blog post.