VII. Fingerprints
There are two national
fingerprint specifications; the FBI's Integrated Automated Fingerprint
Identification System (IAFIS) Image Quality Specifications (IQS) Appendix F and
NIST’s PIV-07 1006. Appendix F has stringent image
quality conditions, focusing on the human fingerprint comparison and
facilitating large scale machine many-to-many matching operation. (FBI Biometric
COE, 2010) Our focus however will be based on the PIV-071006 standard, a lower-level
standard designed to support one-to-one fingerprint verification. The class resolution requirements for
fingerprint capture and use for Personal Identity Verification (PIV) at
Fingerprint Application Profile (FAP) level ten or above are 500 PPI with a
maximum tolerance variation of ± 2%.
Class resolution refers to the resolution required for acquisition or
imaging related use. (Wing, 2011)
Most of the complexity related to
resolution pertains to the friction ridges of the fingerprint. A friction ridge is a raised section of the
epidermis of the skin. A fingerprint is a trace image of the ridges in a human
hand or foot to include the fingers and toes.
Traditionally fingerprints were captured
by rolling the pad above the last joint of the finger and thumbs on an ink
pad and then rolling the inked pad onto a piece of smooth card stock. Impressions of fingerprints are left behind
on various surfaces when the natural secretions of the body, or cosmetic oils
and body lotions, gathered on the ridges are left behind when in deliberate or
accidental contact with any smooth surface. These are referred to as latent
prints. While not always immediately
visible these impressions could be lifted
by dusting the print with specialized powders or exposing the print to chemicals
like silver nitrate or cyanoacrylate ester
and capturing the image by pressing it to a specialized paper or plastic
media. The latent print could then be compared to the inked print with a reasonable
chance for a match determined by an experienced examiner in dermatoglyphics. Although effective this method precludes its
use in identity management based on the sheer volume of prints and comparisons
required. In other words it is not practically
scalable. What is needed is a means of
digitally capturing the fingerprint and storing the resulting record. Live Scan is the most widely used method of
accomplishing this. A live scan involves
pressing or rolling a finger onto a specially coated piece of glass or platen
and then imaging the fingerprint using optical, ultrasonic, capacitive
or thermal imaging to capture the ridges of the finger and the valleys between
them. Optical imaging is in essence a
specialized form of digital photography. The major difference between a digital
camera and an optical imager for capturing fingerprints is the presence of a
light-emitting phosphor layer which illuminates the surface of the finger
increasing the quality of the resulting image.
There are challenging problems when
developing fingerprint recognition systems that use a mobile camera. First, the
contrast between the ridges and the valleys in images obtained with a mobile
camera is low. Second, because the depth
of field of the camera is small, some parts of the fingerprint regions are in
focus but some parts are out of focus. Third, the backgrounds, or non-finger
regions, in mobile camera images are very erratic depending on how the image
captures place and time. (Lee, Lee, & Kim, 2008) So is there an insurmountable
challenge with using a smart phone camera to capture a fingerprint? Image quality is determined by
light quality, lens quality and type, and shutter speed. Smart phones do not fully address each of
these important elements trading size and ease of use for function. Because of this you will get a better
picture from a low end Digital Single Lens Reflex (DSLR) camera than you will
from a high end smart phone camera.
Shutter speed is not an applicable issue with fingerprint capture but
light and lens quality and type are.
An additional challenge
is the probability that one can spoof or fool an optical camera with an image
or impression of a fingerprint. This is
resolved within the industry by using various live finger detector
technologies. One means of live finger
detection is accomplished “by measuring the unique electrical properties of a
living finger that not only characterize the finger print but measure what is
underneath it. This technology has the capability to process the acquired data,
that is, characterize and classify the results in a way that enables the system
to verify a living finger with a very high degree of confidence.” (Clausen & Christie, 2005) It is unlikely that this type of fraud
prevention technology can be integrated into widely available smart phones in
the near future so the risk of fraudulent fingerprints in a mobile identity
management program will have to be addressed through policy or another more
easily implementable technology enhancement.
Despite the obvious challenges, capture of a useable fingerprint image
with a cell phone camera is not impossible.
The operator must take into account the fixed focal length of the camera
lens and make sure the auto focus is disabled in order to get close enough to
capture an image with prominent ridges. Lighting also remains a challenge. An informal test while this paper was being
written used an I-Phone® 4 both with a flash and without. A distance from the camera of four inches
with no flash in a brightly lit room resulted in the best image with clearly
defined ridges in the left index finger of the test subject. By importing the image into paint.net and
using the color inversion tool an image just as clear to the naked eye as one
caught on a live scan was produced. This
test was by no means scientific but serves as an indicator that it is not a far
stretch to utilize off the shelf cell phone technology. The methodology of the image capture is not necessarily
a limiting factor even taking into account challenges with optics and
lighting. The recognition algorithms
used in the associated databases can counter or resolve some of the
issues. Many fingerprint recognition
algorithms perform well on databases that had been collected with
high-resolution cameras outperforming feature only searches by trained
examiners. (Indovina, Hicklin, & Kiebuzinski, 2011)
VIII. Face
Facial recognition is considered
to be the most immediate and transparent biometric modality when it comes to
physical authentication applications. Why
is it that many people are inclined to give up their facial image without
question while the concept of giving up a fingerprint causes them great
discomfort and angst. Facial recognition
is a modality that humans have always depended on to authenticate other
humans. We are in essence hardwired for
facial recognition. Therefore the addition
of facial recognition through or enhanced by technology is an easy one to
accept. “Whether or not faces constitute
a [special] class of visual stimuli has been the subject of much debate for
many years. Since the first demonstrations of the Binversion effect…it has been
suspected that unique cognitive and neural mechanisms may exist for face
processing in the human visual system.” (Sinha, Balas, Ostrovsky, &
Russell, 2006)
Facial recognition as a
technology is one of the most mature of the biometric modalities. It is also relatively simple from the image
capture standpoint. Capture of a facial
image requires little or no cooperation from the subject making it the
technique of choice for passive applications like those used in airports and
casinos. On the surface it seems as
though all of the issues are algorithm related but as our concept is focused on
a cell phone camera as our capture device this is not really the case.
We previously discussed the
megapixel issue but megapixel capability has no discernible impact on the
biggest challenges with facial recognition which are image capture and pose
correction. Image capture is a light and
optics issue. One of the biggest
drawbacks to smart phone cameras is the size of the sensor. Camera technology has changed but the basic
principles have applied since the first tin types were produced in the mid 19th
century. The sensor is the replacement
to the emulsion based films. The larger
the sensor the more light it can detect resulting in better picture quality. Smart phone cameras have a much smaller
sensor than the traditional 35mm film size and as a result have a smaller angle
of view when used with a lens of the same focal length. This results in an image that is essentially
cropped. In order to adjust for this the
camera must be further back from the subject posing problems related to
lighting and detail.
Facial recognition software analyzes
a number of structural facial elements. Examples
of these distinctive surface features include shape of the eyes and the eye
sockets; the width, length, and structure of the nose; the thickness of the
lips, and the width of the mouth. What
is common about all of these elements is that they are three dimensional. A camera captures images in two dimensions.
The difference between a three dimensional subject and the two dimensional
output of the cameras is handled by the software but pose issues including
expressions, external features, background, and lighting all add variables that
decrease the effectiveness of the algorithms. In the home environment it may be
difficult to deal with lighting and background issues but this is not an
insurmountable challenge. In the same
manner external features such as beards, glasses, jewelry, and piercings can
all pose problems. The author of this
paper has endured lengthy picture sittings in front of DSLR cameras for PIV
credentials. It seems his white goatee
gives the capture software conniptions.
This serves to demonstrate that issues of facial capture are not
necessarily specific to smart phone cameras.
Many of the issues in facial
image capture would be solved if the images could be captured in 3D. Of course this would eliminate the use of
smart phones as a capture device, or would it?
Fujitsu continues to refine a way
for phones that just have one rear camera to shoot three-dimensional videos
with the aid of a special attachment.
The attachment uses mirrors to send two different images to the camera’s
sensor and is smaller than a stick of Chap Stick. In June of 2011 Sprint released the HTC Evo
3D 4G 'Gingerbread' Smartphone. This
phone had two integrated cameras capable of taking 3D pictures. With the potential of standard 3d capture
technology on the horizon it may not be long at all before changes in lighting
and camera angles become irrelevant.
Three dimensional image captures can only serve to enhance the potential
of fingerprint capture as well. Even the
issue of software sensitivity to expressions, one not mitigated by 3D
technology, could soon be eliminated. As
far back as 2004 Technion, the Israel Institute of Technology, a public
research university in Haifa researched using metric geometry to address the
issue of expression sensitivity. The
approach was to use metric geometry isometrics to create an expression
invariant three dimensional face recognition solution. (Bronstein, Bronstein, & Kimmel, 2004)
IX. Why not?
There are other biometric signatures
that have both been the focus of research and have seen increased use and
acceptance from the physical and logical access communities. Iris scans, hand geometry, and voice
recognition are no longer the purview of James Bond and Ethan Hunt. Although not practical for this smart phone
centric premise they are worth mentioning and potential near future candidates.
Iris scans are based on the stability
of the trabecular meshwork, an area of tissue in the eye located around the
base of the cornea. The patterns are
formed by the elastic connective tissues which gives the iris the appearance of
radial divisions which are unique and often referred to as optical fingerprints.
Iris sampling offers more reference coordinates than any other biometric
resulting in an accuracy potential higher than any other biometric. Iris scans require a high degree of
cooperation from the subject from whom the sample is being acquired. Today specialized capture devices are
required. Despite their complexity these
capture devices are nothing more than still cameras capturing very high quality
images. It is certainly not out of the
realm of possibility that a smart phone camera could one day soon be capable of
the required performance.
Hand biometrics is a fairly mature
technology that lends itself to applications where the size of the capture
device is not a factor. Current devices
are based on charge-coupled device (CCD) optical scanning and consistently
deliver better quality images than fingerprint scanners. This is largely due to the increased sample
size, your hand being many times larger than a finger pad. Three-dimensional photography may show some
promise as an alternative method of hand biometric image capture in the
future. Current technology remains
expensive and not at all compatible with the proposed smart phone format.
Voice recognition is perhaps the oldest
form of biometric identifier. Not to be confused with speech recognition, which
is the process of translating speech into text, voice recognition is the
process of identifying someone from their voice patterns. It is a phenotype, an observable behavior
influenced by development, often with regional characteristics. Of all of the fields of biometric research,
speech development has seen the most modern day focus with significant research
over the last four decades. Voice
recognition has some uniquely distinct advantages over other biometric
signatures in that it can be combined with pass phrases, knowledge based
verification, or can be used as a passive background tool. Voice recognition is the least invasive and
is easy on the user. With all this it
would seem like speech recognition should be the biometric of choice but has
its disadvantages. Voice recognition programs
take the digital recording and parse it into small recognizable pieces called
phonemes. These phonemes may not be
consistently reproduced as they can be influenced by behavior and health
factors and even background noise.
Works Cited
Bronstein, A. M., Bronstein, M. M., & Kimmel, R.
(2004). Three-Dimensional Face Recognition. Technion, Israel Institute
of Technology, Department of Computer Science. Kluwer Academic Publishers.
Clausen, S., &
Christie, N. W. (2005). Live Finger Detection. IDEX ASA. Fornebu,
Norway: IDEX ASA.
FBI Biometric COE.
(2010, April 27). FBI Biometric Specifications FAQ. Retrieved May 31,
2012, from FBI Biometric Center of Excellence: https://www.fbibiospecs.org/iafis_FAQ.html
Indovina, M., Hicklin,
R. A., & Kiebuzinski, G. I. (2011). Evaluation of Latent Fingerprint
Technologies: Extended Feature Sets [Evaluation #1]. U.S. Department of
Commerce, National Institute of Science and Tecnhology. Washington D.C.: US
Government Printing Office.
Lee, S., Lee, C.,
& Kim, J. (2008). Image Preprocessing of Fingerprint Images.
Biometrics Engineering Research Center at Yonsei University., Korea Science
and Engineering Foundation, Seoul, Korea.
Sinha, P., Balas, B.,
Ostrovsky, Y., & Russell, R. (2006). Face Recognition by Humans: Nineteen
Results All ComputerVision Researchers Should Know About. Proceedings of
the IEEE , 94 (11), 1957.
Wing, B. (2011). Data
Format for the Interchange of Fingerprint, Facial & Other Biometric
Information. US Department of Commerce, National Institute of Science and
Technology. Gaithersburg: US Government Printing Office.
