Face Recognition Algorithm Finally Outperforms Humans

KentuckyFC (1144503) writes "Face recognition has come a long way in recent years. In ideal lighting conditions, given the same pose, facial expression etc, it easily outperforms humans. But the real world isn't like that. People grow beards, wear make up and glasses, make strange faces and so on, which makes the task of facial recognition tricky even for humans. A well-known photo database called Labelled Faces in the Wild captures much of this variation. It consists of 13,000 face images of almost 6000 public figures collected off the web. When images of the same person are paired, humans can correctly spot matches and mismatches 97.53 per cent of the time. By comparison, face recognition algorithms have never come close to this. Now a group of computer scientists have developed a new algorithm called GaussianFace that outperforms humans in this task for the first time. The algorithm normalises each face into a 150 x 120 pixel image by transforming it based on five image landmarks: the position of both eyes, the nose and the two corners of the mouth. After being trained on a wide variety of images in advance, it can then compare faces looking for similarities. It does this with an accuracy of 98.52 per cent; the first time an algorithm has beaten human-level performance in such challenging real-world conditions. You can test yourself on some of the image pairs on the other side of the link."

Only Outperformes Humans in 1 of 5 Datasets

[HannethCom]

Both the title and description are sensationalism. If you read the paper, the algorithm works at 98.52 on 1 of 5 data sets. We could also accurately say the algorithm is only 89.33% accurate. The score from the most difficult database. Much worse than the normal person.

That being said, it is much better than other publicly available algorithms.