This relative measure of pixel brightness makes it much easier to recognize the same face as being the same face across multiple different lighting situations. Relative lighting attributes tend to hold true between shots, while objective lighting is much more variable — but even with this and other techniques, widely varying lighting conditions are still a point of difficulty for many modern facial recognition systems. They also present difficulties for human judgement of faces, it should be noted.
Facial biometrics – fascinating and intriguing
Wrapping a face around a third dimension can often reveal forms of symmetry and distinguishing characteristics that are much harder to find in a flat and static image. Thus, when shown a photograph of Leonardo DiCaprio, this sort of system would first warp and analyze the photo in various ways to generate an encoded version, then compare that encoded face against a collection of encoded faces on file. Even with tricks like encoding, though, human software engineers have been incapable of creating sufficiently fast and accurate processes for comparing two encoded faces and determining whether they are similar enough to be deemed the same person.
To achieve this, we need a labeled machine learning dataset: Microsoft Azure Face Identification Demo. So, a facial recognition dataset might be a collection of photos of human faces — along with some photos of animal faces and face-like objects that are not faces at all. Each of the photos in the dataset will be appended with metadata that specifies the real contents of the photo, and that metadata is used to in validate the guesses of a learning facial recognition algorithm.
Compiling the datasets to be used by a machine learning system is often far more time-consuming and expensive than actually using those datasets to train the system itself. Correct guesses very slightly increase the likelihood that the approach that led to the correct guess will be used again in future runs, while incorrect guesses slightly decrease the same.
Future generations will scoff at your passcode.
These deep learning solutions have brought facial recognition into the 21st century. Today, advanced facial recognition technology is working its way into crucial security processes at banks, and the less-crucial ones in consumer mobile phones. That explosion in facial recognition uses has sparked a real need for large and comprehensive new image and video datasets to use to train the machine learning systems to meet the incredible demand for AI products.
In the dash to build these databases , some companies are starting to go with the lowest bidder, and running into issues like rushed image quality which can dramatically impact learning efficiency. Poor-quality datasets can also introduce biases to the final product; if a facial identification system is trained on racially homogenous pictures, it will end up being worse at identifying people of those races it has yet to see. Facial recognition technology is already being used to help with searches for criminal suspects and with judgement of job interviewees; Microsoft even has an easy-to-use middleware solution AI emotion analysis, so just about anyone can work advanced sentiment analysis into their projects.
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Related Biometrics: A Look at Facial Recognition
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