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The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. Perhaps you have heard about Deep Learning as a new frontier in several machine learning applications which is to be hoped that will fundamentally reshape what computers can do (If you have not heard about it so far, what can I say, perhaps you were living in a cave!).
Yaniv Taigman, Ming Yang, Marc Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Activities on fingerprint-based biometric systems increased in both academia and industry as several research groups and companies developed new algorithms and techniques for fingerprint recognition and as many new electronic fingerprint acquisition sensors were launched into the marketplace. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine.
Deep Learning is especially suitable for dealing with large training sets and has been recently applied in diverse domains such as vision, speech and language modeling.
The aim of this initiative is to take the first steps toward the establishment of a common basis, both for academia and industry, to better understand the state-of-the-art and what can be expected from this technology in the future.In FVC2000, four different "sensors" were used to cover the recent advances in fingerprint sensing techniques. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets.



The Facebook team adopted this tool to apply to their face verification algorithm in lieu of well engineered features which is common in majority of contributions in this field. In fact, databases 1 and 2 were collected by using two small-size and low-cost sensors (optical and capacitive, respectively).
As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. Finally, images in database 4 were synthetically generated by using the approach described in:R.
This puts the DeepFace accuracy at the brink of human level accuracy (97.53%) in face verification.
More information on the performance of the state of the art face recognition algorithms can be found here.DeepFace is trained on a large dataset of faces, an identity labeled dataset of four million facial images belonging to more than 4,000 identities, acquired from a population vastly different than the one used to construct the evaluation benchmarks. In other words, each identity had an average of a thousand samples.The DeepFace algorithm, first aligns a face so that the person in the picture faces forward, using a 3-D model of an “average” forward-looking face.
Maltoni, "Synthetic Fingerprint-image Generation", proceedings 15th International Conference on Pattern Recognition (ICPR2000), Barcelona, September 2000.Each database was split into a sequestered "test" set of 800 images and an open "training" set of 80 images made available to participants for algorithm tuning.


Then it uses the deep learning to find a numerical description of the forward-looking face. If two different images have similar enough descriptions, DeepFace decides they must show the same face.According to Facebook, DeepFace remains purely a research project for now. Theoretically, the algorithm could be expanded to cover a much larger identities, and then be applied to Facebook’s social network itself.
This technology would be very useful for Facebook (not necessarily for us!) since it will automate the process of identifying all its user's contacts without the use of manual tagging, which could be a great source of information to feed into a Big Data analysis.



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