“Holy Bat-phone, Batman!”

NEC develops biometrics technology that uses sound to distinguish individually unique ear cavity shape (NEC)

The new technology instantaneously measures (within approximately one second) acoustic characteristics determined by the shape of the ear, which is unique for each person, using an earphone with a built-in microphone to collect earphone-generated sounds as they resonate within ear cavities. This unique method for extracting features is useful for distinguishing individuals based on acoustic characteristics and enables rapid and highly accurate recognition (greater than 99% accuracy).

 

Amazon envisions another way to unlock a phone: Ear photos

Forget Fingerprint Scanners, Amazon is Interested in Using Your Ears to Unlock the Phone — Here’s Why it’s Better (Technology Personalized)

The world’s largest e-commerce company was granted a patent last week that reveals company’s intention to ease up the unlocking mechanism in a phone when a user receives a call without any security tradeoff.

No need to forget fingerprint scanners just yet, though.

We’re all ears

New academic research on ear biometrics…

3D Ear Identification Based on Sparse Representation (Forensic Magazine)

Compared with classical biometric identifiers such as fingerprint and face, the ear is relatively a new member in the biometrics family and has recently received some significant attention due to its non-intrusiveness and ease of data collection. As a biometric identifier, the ear is appealing and has some desirable properties such as universality, uniqueness and permanence. The ear has a rich structure and a distinct shape which remains unchanged from 8 to 70 years of age as determined by Iannarelli in a study of 10,000 ears. The recognition using 2D ear images has a comparable discriminative power compared with the recognition using 2D face images.

If you click through to the whole study at plos.org, the authors (Lin Zhang, Zhixuan Ding, Hongyu Li & Ying Shen) have made the Matlab source code for the ear matching algorithm available. That’s really neat.

From our first post on ear biometrics in 2010…

Pros:
-Facial recognition accuracy is degraded as the pose angle diverges from a full frontal view. As pose angles get bigger, an ear will come into view. Tying an ear-recognition system to a face recognition system could make more identifications possible, especially with a non-participating subject.

Cons:
-Ears aren’t really that stable. They grow throughout life, as the quote above addresses.
-As high school wrestlers can attest, ears are easily deformed by trauma.
-Hair obscures significant portions of the ear in a significant percentage of the population.

The challenges confronting any new biometric modality

[ed. This post reflects a substantial rewrite of an earlier post of January 24, 2013: Not the bee’s knees]

Every once in a while a version of the following paragraph finds itself in the news…

Biometrics Using Internal Body Parts: Knobbly Knees in Competition With Fingerprints (Science Daily)

Forget digital fingerprints, iris recognition and voice identification, the next big thing in biometrics could be your knobbly knees. Just as a fingerprints and other body parts are unique to us as individuals and so can be used to prove who we are, so too are our kneecaps. Computer scientist Lior Shamir of Lawrence Technological University in Southfield, Michigan, has now demonstrated how a knee scan could be used to single us out.

Forget digital fingerprints, iris recognition and voice identification, the next big thing in biometrics could be your ______________.

Examples are numerous and fecund:

Heartbeat?
Rear-end?
Ear?
Bone structure or electric conductivity?
Footsteps?
Nose? (ed. Link added later. I forgot about that one.)
Body odor?
Brain prints?
Lip movements?
Kneecap?

While I suspect that any definable aspect of the human anatomy could be used as a biometric identifier — in instances where teeth are all that is known about an individual, they are used for high confidence identification — I’m afraid that, for the foreseeable future, the cards are stacked against any new biometric modality catching on in any big way.

The reasons for this are both scientific (research based) and economic (market based).

On the science side, a good biometric modality must be: unique, durable, and easily measurable. If any of these are missing, widespread use for ID management isn’t in the cards. If something is unique and durable but isn’t easily measurable, it can still be useful but it isn’t going to become ubiquitous in automated (or semi-automated) technology. Teeth and DNA fit this model. Teeth have been used to determine the identity of dead bodies with a high degree of certainty for a long time, but we aren’t going to be biting any sensors to get into our computers any time soon — or ever. Likewise with DNA.

There is also the challenge of proving that a modality is in fact unique, durable and easily measurable which requires a whole lot of experimental data and (especially regarding uniqueness) a healthy dose of statistical analysis. I’m no statistician, and from what I understand, the statistical rules for proving biometric uniqueness aren’t fully developed yet anyway, so let’s just leave things in layman’s terms and say that if you’re wanting to invent a new biometric modality and someone asks you how big a data set of samples of the relevant body part you need, your best answer is “how many can you get me?”

In order to ascertain uniqueness you need samples from as many different people as you can get. For durability you need biometric samples for the same person taken over a period of time and multiplied by a lot of people.

Ease of measure is more experiential and will be discovered during the experimentation process. The scientists charged with collecting the samples from real people will quickly get a feel for the likelihood that people would adapt to a given ID protocol.

For two common biometric modalities, face and fingerprint, huge data repositories have existed since well before there was any such thing as a biometric algorithm. Jails (among others) had been collecting this information for a hundred years and the nature of the jail business means you’ll get several samples from the same subject often enough to test durability, too, over their criminal life. For face, other records such as school year books exist and were readily available to researchers who sought to test the uniqueness and durability of the human face.

The first hurdle for a novel biometric modality is the competition for the attention of scientists and researchers. Getting the attention of science and technology journalists by making a pronouncement that the space between the shoulder blades is the next big thing in biometrics is one thing. Getting academic peers to dedicate the time and research dollars to building the huge database of interscapular scans required for algorithm development is quite another. Any new modality has to offer out-sized advantages over established modaities in order to justify the R&D outlay required to “catch up”. This is highly unlikely.

On the market side, in order to displace established (finger/hand and face/eye) biometric modalities in wide scale deployments, the academic work must be complete and the new technology must produce a return on investment (ROI) in excess of that offered by existing technologies designed to accomplish the same function.

That’s not to say that modalities that didn’t have the advantage of a 100 year head start on data collection are impossible to bring to market. Iris, voice, and the vascular biometrics of the hand (palm, finger) have joined face and fingerprint biometrics in achieving commercial viability despite the lack of historic data repositories. But there were several things recommending them. They either occupy prime real estate on the head and the end of the arm (Iris, vein) making them easy to get at, or they are the only biometric that can be used over a ubiquitous infrastructure that simply isn’t going anywhere (voice/phone), or they offer advantages over similar established modalities. With hand vascular biometrics: they’re harder to spoof than fingerprints; no latency; avoidance of the “fingerprinting = criminality” stigma; can work with gloves; users can avoid touching the sensor, etc. With iris: harder to copy than the face; harder to spoof; easier to measure than retina vasculation; and extremely low/no latency. Yet even despite gaining the required academic attention, iris and voice have had great difficulty overcoming the market (ROI) hurdle, which brings us back to knees.

Is there any database of kneecaps of significant size to allow researchers to skip the time-consuming task of building such a database themselves reducing the cost of development? Is there any deeply embedded ubiquitous infrastructure that is already an ideally suited knee-sensor? Is there any objection to modalities that have a head start on knees that knee biometrics would overcome? Is there any conceivable, repeatable, scalable deployment where a potential end user could save a whole lot of money by being able to identify people by their knees? I’m at a loss but these are exactly the kind of questions any new biometric modality must be able to answer in the affirmative in order to have any hope for wide-scale deployment.

So, it’s pretty clear that knee biometrics are not something the average person will ever come into contact. Does that mean there is no value in exploring the idea of the kneecap as a feature of the human anatomy capable of being used to uniquely identify an individual? Not necessarily.

In order to thrive as high value-added tools in highly specialized deployments a novel modality just needs to help solve a high value problem. This has heretofore been the case with teeth & DNA. The analysis of teeth and DNA is expensive, slow, requires expert interpretation, and is difficult to completely automate, but has been around for a long, long time and isn’t going anywhere anytime soon. That’s because the number of instances where teeth and DNA are the only pieces of identifying information available are frequent enough, the value of making the identification is high enough, and the confidence level of the identification is high enough that people are willing to bear the costs associated with the analysis of teeth and DNA.

Beyond teeth and DNA, any biometric modality can be useful, especially when it is the only piece if information available. The CIA and FBI even invented a completely novel biometric approach in an attempt to link Khalid Shaikh Mohammed to the murder of Daniel Pearl using arm veins. But how likely is something like that ever to be the case for any of these novel modalities, knees included? It’s possible that the situation could arise where a knee bone is discovered and there is an existing x-ray or MRI of a known person’s knee and a comparison would be useful. That, however, is not enough to make anyone forget about any already-deployed biometric modality.

Because it’s been a while since we mentioned ears as a biometric modality…

Progress In Unconstrained Ear Recognition (Science 2.0)

The Southampton team have developed a new technique using scale-invariant feature transform and homographies calculated from SIFT point matches. It can cope not only with fuzzy and degraded images, but also with ears that are up to 18% occluded.

A healthy degree of skepticism is in order regarding the short and medium term prospects for the adoption of more exotic biometric modalities: ear, gait, gluteus maximus, etc. In order to make it into commercial applications (as opposed to forensic applications) either ear biometrics are going to have to accomplish an ID task more accurately and more cheaply than the more established modalities (face, finger, etc. which have a head start) or they’re going to have to facilitate identifications that the others can’t.

Maybe ear biometric verification on a mobile phone’s front-facing camera can work.

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