In the last 20 years of research hundreds of machines have processed these faces and we can still find some results spread in the Internet, but their faces are known more by machines than by human beings.
This animated GIFs serie wants to bring back their image into existence.
“The Olivetti faces dataset”
(from top left to bottom right: s02, s03, s04, s05, s07, s08, s11, s13, s19, s20, s22, s25, s29, s35, s37)
The pictures were shot at The Olivetti Research Laboratory (ORL) and the subject are either Olivetti employees or Cambridge University students. Subjects were asked to face the camera and no restriction were imposed on expression, varying the lighting, open/closed eyes, smiling /not smiling, glasses/no glasses. All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position. The artwork presents 10 animated gif selected from the subjects into the database. Some of them are really funny!
Animated gif series based on: “AT&T The Database of Faces” , a set of face images taken between April 1992 and April 1994 at the lab, also known as “The Olivetti faces dataset”
“The Yale Face Database”
(with or without glasses, different lights and expressions)
from top left to bottom right: subject01, subject02, subject03, subject04, subject05, subject06, subject07, subject08, subject09, subject10, subject11, subject12, subject13, subject14, subject15.
Animated gif series based on: “The Yale Face Database” – “There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink.”
Face Recognition under Variable Lighting
A series of animated gif featuring human subjects under 64 illumination condition. This selection of 10 human subjects come from The Extended Yale Face Database B
from left to right: B01, B02, B05, B06, B09, B12, B21, B22, B34, B38
Based on: “The Extended Yale Face Database B”, 16128 images of 28 human subjects under 9 poses and 64 illumination conditions.
How to recognize hand gestures
Try to recognize 10 different hand gesture on a uniform background.
Animated gif series based on: The NUS hand posture datasetshttps://www.ece.nus.edu.sg/stfpage/elepv/NUS-HandSet/
based on: The Third International Competitions on Fingerprint Verification.
The aim of FVC2004 was to track advances in fingerprint verification and to benchmark fingerprint-based systems, both in matching techniques and sensing devices.
How to detect a cow walking left
Side view cow facing left (2017)
Based on: The TU Darmstadt Database