fHouston (TX) - Scientists at the University of Houston are researching an advanced form of 3D facial recognition. They're hoping to prove this technology powerful enough to serve as a foundational security component, one possibly working alongside other biometrics. More secure than PINs, faster and easier than swiping a card, and less invasive than being thumbprinted, 3D facial recognition systems could be the security breakthrough which keeps us all from being chipped.
TG Daily recently had the opportunity to visit the University of Houston to see state of the art research in this field. Dr. Ioannis Kakadiaris leads the team and was on hand with Ann Holdsworth from the office of communication to give me a guided tour through his research facility. Dr. Kakadiaris' work is considered to be the bleeding edge of this technology discipline. And although it is a young science, it could well solve the problem of identity theft and provide enhanced personal security.
Data points
Most facial recognition systems today rely on data obtained from 2D images. Emerging 3D prototype recognition systems are expected at a faster pace now that more parallel compute abilities exist. In fact, in 2004 the U.S. government recognized this trend and issued a set of guidelines for the minimum requirements of facial recognition software. These baseline a level to which all future offerings must equal or exceed.
The technology we observed is called "URxD", which stands for Ultimate Recognition in X Dimensions. The name was chosen as Dr. Kakadiaris' technology can be extended to include additional data whenever new input abilities are made available. This does not limit it to 2D or 3D images, but can include even future, currently unknown technologies. According Dr. Kakadiaris, the basic rule for this extensible idea is simple: The more input data is provided, the more accurate the system is.
Dr. Kakadiaris' prototype system considers many factors when determining if you are really you. Today, these include an reconstructeded data set of more than 120 computed factors from 3D landscapes and images. The factors describe many facets relating to the front half of your head as is observed by their image capture ability.
Facial scanning
The idea behind facial recognition is quite interesting. Basically, the biggest advantage is that no matter where you are, your face is also there. Dr. Kakadiaris said no two people look exactly alike, and that's especially true when observed through the infrared spectrum. Even identical twins have some differences. His team is working on twin research right now, but he was unable to share his findings with us at the time of our visit (because the tests are incomplete).
When his technology is combined with another very simple form of non-invasive biometrics, such as voice recognition, the ability to show your mug and talk could become a very powerful way of identifying yourself. It would be a security mechanism which could not be stolen, and one that's always with you without being invasive.
2D, 3D and xD
Many existing facial recognition systems rely on 2D images. They look for various data points visible through a 2D photograph. This 2D system is often the result of keeping costs down. 2D cameras and software algorithms are readily available at low cost making them very attractive. But, they can be fooled. For some of them, even holding up a large, quality photograph of someone can fool the system.
Recognition technologies include search criteria such as the distance between the eyes, eye socket height, width, eye angle, nose length, multi-point nose width, cheek position, size, shape, jawline, size, shape, temple shape, depth, overall face shape, relative distances across and up/down the face, lip thickness, height, width, contour, texture, and about 60 more attributes and various combinations thereof. When assembled and pieced together these comprise a set of data points which is stored in a database and can be compared as necessary.
Dr. Kakadiaris' research effort takes this process a few steps further by moving from just 2D data into xD data. He describes each new form of data as providing "additional dimensions". When infrared is added, it increases the dimensions to 5D (3D + 2D for the infrared image). When the full shape is considered, and not just 3D rendered texture data, he calls it 7D (2D texture + 2D infrared + 3D shape). It is 8D if time is added.
Data points in 3D are no longer limited to just those generally observable from two dimensions. With 3D the entire surface structure of the face becomes a mine for data points. That mine includes both textures and shapes. In fact, when you see the full contour of your face shown on-screen in a movable manner, it's almost freaky how real it looks. What was previously a technology using only a flat image with certain attributes known and understood from a single vantage point, now with 3D it becomes much more alive. A raised image holds not only the same set of original attributes, but also a whole new set of attributes derived from the 3D facial landscape.
As additional data points are included, such as infrared and motion, an "average sample" can be constructed. The database search then becomes much more representative of the real face as it is based on more data, all of which makes errors less pronounced.
3D cameras
The imaging process begins with the subject moving into a field of view where the camera system captures the image. The 3D camera system is a set of 2D cameras arranged at a series of specifically spaced positions. When their multi-view 2D images are run through special software algorithms, they reconstruct an amazingly accurate 3D model of the whole front half of your head from a relatively small camera box (about the size of a large box of breakfast cereal).
Dr. Kakadiaris also has a type of stereoscopic 3D camera, a system that includes a pair of 3D cameras pointing toward the subject's face. It's connected by a rod and provides a greater field of vision. Though, because its field of view is larger it can capture not only the front of your head, but also much of the sides as well.
All of this collected data is used in the prototype. It includes high-definition textures, a full-frame wire mesh showing the shape, as well as a complete landscaping or topography view visible via shading. The images it produces of the capture face, when rendered and moved around in 3D, are quite stunning.
The capture system is not limited to just 3D, however. The X-Dimension system also allows this data to be merged with other sources. The most obvious is infrared which reads the thermal data from your face. On the day we were there the infrared cameras had been loaned out to another department so that information was not available to us. However, from Dr. Kakadiaris' stock material we can see what it would look like. By rendering the captured data into the visible spectrum, a person's face takes on a whole new appearance.
The thermal data provide not only additional data points, but it's really like a true facial fingerprint. When combined with the 3D image, even if two people look almost identical on the outside, the aspects and qualities of their facial thermal terrain can be used to differentiate and identify them very easily. This data set makes the process far more accurate than the 3D image data alone. But, it's not even limited to infrared.
Laser scans are also available as an alternate source of 3D input. And, as mentioned, motion capture can also add additional data points which allow the recognition system to become more accurate. Again, the more points sampled the more accurate.
Read on the next page: Scan time, Operations, Accuracy, Capture software