CRIMINALS who go under the knife in an effort to evade capture might want to consider an alternative disguise, thanks to a new technique for matching faces before and after plastic surgery.
Typical facial-recognition software can be thrown off by even minor changes in the lighting and position of an unaltered face. Post-surgical matching is even harder for obvious reasons, says , a computer scientist at the University of Notre Dame, Indiana, whose team developed the new system: 鈥淚f someone has plastic surgery, they鈥檙e trying to change the appearance of one or more parts of their face.鈥� As a result, existing software鈥檚 success rate can be cut in half when trying to match before and after photos gathered from plastic surgery websites.
Bowyer鈥檚 colleague, Gaurav Aggarwal, realised that matching individual facial features rather than whole faces could be more successful.
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Aggarwal was inspired by a facial-recognition technique called sparse representation, which matches an image of a face by comparing it with combinations of individual features from faces already recorded in a database. If the closest matching combination turns out to be made up of features mostly drawn from one person in the database, it is a good bet to say the target image is also of that person. But if the best match combines features pulled from images of many different people then the system has failed to identify the new face.
However, to function properly sparse representation requires multiple images of each person in the database, so it does not work with pairs of before and after surgery pictures alone. The new system does. It uses two databases: a general one full of random faces, and another containing all of the 鈥渂efore鈥� pictures 鈥� akin to police mugshots. When a target 鈥渁fter鈥� picture is analysed, a composite picture as similar as possible is created from the features of people in the general database. All of the 鈥渂efore鈥� pictures go through the same process. If the composite picture created using the 鈥渁fter鈥� picture matches closely with any of the composite pictures derived from the 鈥渂efore鈥� pictures, the two are declared a match.
The team found that while surgery changes the appearance of a face, many individual features stay the same, and matching based on the nose or eyes alone was actually more accurate than some existing whole-face techniques. Combining the matches of all facial features gave the team a 78 per cent success rate when comparing pre- and post-surgical photos. They presented their work this week at the , Colorado.
鈥淭hey鈥檙e on the right track,鈥� says of , a company based at the University of Kent, UK, which provides facial ID software to police. He says the new approach could help police uncover disguised criminals but is unlikely to ever be totally accurate.
