Artificial neural network models can make amazing computations. These models are explained along with their application in problems associated with fighting crime. Specific problems addressed are identification of people using face recognition, speaker identification, and fingerprint and handwriting analysis (biometric authentication)

Kabrisky, Matthew

Oxley, Mark E.

Rogers, Steven K.

Ruck, Dennis W.

English

NASA Technical Reports Server

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How Captain Amefika Uses Neural Networks to Fight Crime
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Steven K. Rogers, Matthew Kabrisky, Dennis W. Ruck and Mark E. Oxley
Air Force Institute of Technology
Deparunent of Electrical and Computer Engineering
2950 P Street, Wright-Patterson AFB, OH 45433-7765
15 February 1994
Abstract
Artificial neural networks models can make amazing computations (some of which
are applicable to fighting crime: recognition of faces; speaker identification; fingerprint
recognition). Those models will be explained along with the application of those models
into problems associated with fighting crime. Specific problems addressed are
identification of people using face recognition, speaker identification as well as fingerprint
and handwriting analysis (biomelric authentication).
I Introduction
Before getting started it is common to explain the Captain Amefika connection.
Captain America comic books describe the superhero as: "born in the U.S.A,"
that obviously applies to the authors; "endowed with a superhuman physique," once you
see the authors at the conference you will make the obvious connection with this point;
and finally "fights an ongoing battle for liberty, justice, and the American dream!", who
needs Ross Perot? Oh, by the way, you might also notice in the comic book that Captain
America's secret identity is "Steve Rogers". The "k" in Captain Amerika is just a
copyright infidngement worry of that author.
This lecture covers the application of artificial neural network techniques for
fighting crime. For example the image of a suspect might be provided to some law
enforcement agency for processing, possibly to recognize the person in the image. Image
processing usually consist of three stages. The first is the location of regions of interest
within the image (segmentation-find the face). The second step is the extraction of a set
of numbers which characterize the regions that are extracted (feature extraction-describe
the face). The last step is the processing of the features for
decision making (classification-decide who it is).
II Crime Fighting Problems
An enormous part of crime fighting is recognition of faces. We will use this
problem to demonstrate the application of artificial neural networks to real world
problems. During the lecture other problems like fingerprint identification, speaker
identification and handwriting analysis will also be addressed. From automatic mugshot
matching to border crossing monitoring, law enforcement agencies need an autonomous
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face recognition capability. S_uch a system could als_obe used to verify users of automatic
teller machine cards, or control of login into sensitive computer systems. This capability
has also been used to interface handicapped people to computers. To be honest this last
application is the one that our group is the most excited about. In this case a young
Chicago lady (13 years old) Who has cerebral palsy was interfaced to her personal
computer by recognizing her facial expressions.
Ill Segmentation
The finding of regions of interest in an image is called segmentation-find the face in
the image. Any errors in this step are preferred to be false acceptance, (passing pixels that
may not contain parts of the face), but not false negatives (miss regions that might contain
parts of the face). The same concept applies to processing sound. For example, when
trying to identify a speaker's voice, sound is recorded. The parts of the recording that
need to be identified must be segmented from the rest of the recording. To be of any
benefit, this step must significantly reduce the number of pixels or periods of the recording
that the next steps of feature extraction and classification must deal with. The processing
of the raw pixels to find the regions that might contain the face may be the toughest of the
image processing stages. To reduce the amount of computation necessary for the
subsequent processing the system should only look in those regions of space, time,
frequency, intensity or texture where the face is likely to be located. A one-pass
segmentation algorithm filters the raw data to eliminate obvious nonface regions (a
function of neighborhood calculations).
Before feature extraction, image preprocessing is usually necessary. The most
common preprocessing is some form of energy normalization. The preprocessing is
necessary because images have characteristicaUy low contrast and lots of irrelevant
structure. To be effective for-_al world images, the energy normalization is usually based
on local neighborhood information. Most segmentation techniques are based on
morphological operations, texture analysis and l_al intensity comparisons or spatial
frequency information processing that allow discrimination of regions of interest from the
rest of the pixels.
Single neurons can be probed by electrodes and stimulus response measurements
made. The results of such measurements show that the system cares about local
orientation information and motion direction. Similar more recent measurements have
expanded this idea to localized texture information as being the critical fast step. To get
information from multiple locations, radioactive dyes have been used and clearly show the
mapping of the real world onto the visual cortex. One problem with these experiments is
that the animal has to volunteer to have its metabolism reduced to zero for the
measurements. Only volunteer animals are used of course. Using VLSI technology,
multiplexed array cortical electrodes have recently been made and implanted directly onto
cortex.
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IV Feature Extraction
The processing of the data to extract a set of measurements (describe the face) that
represent the gestalt of the information required to decide who is in the image is called
feature extraction. There can be no information gained by this step; its purpose is to
increase the ratio of pertinent information to irrelevant data. If a perfect classification
stage could be accomplished on the raw data, it would achieve the lowest error possible.
But, in the problems of interest here, image processing for face recognition, the processing
of the raw data (the original images) is not always feasible. The dimensionality alone of
such a task make it not an option for some applications. For each region of interest
segmented, a set of features must be found to represent the region for classification.
There are several popular methods for obtaining the features to be used. The first
is to ask experts in the field of interest. For example in the problem of target recognition
some common features include: length-to-width ratio; hot spot intensity; or complexity.
Similarly, relevant expert extracted features are used in face recognition, such as the
distance between anthropometrically significant features. The distance between the eyes
or from the bridge of the nose to the chin. No one believes that computer aides for
recognition are useful if human extracted features have to be keyed in. Finding the
important parts of the face by using artificial neural networks is a key first step.
The second alternative is to have the segmented regions processed directly by the
neural feature extractor. One common neural feature extraction technique uses a layer of
artificial neurons with receptive fields in the input raw data. This is similar to the
processing discovered in visual striate cortex, V1. The Nobel Prize winning results of
Hubel and Wiesel clearly demonstrated that orientation selectivity and motion direction
selectivity within the receptive field of a striate neuron exists. The weights for these
artificial neurons are either found using a gradient search based learning algorithm,
likrdwired based on some a priori knowledge (such as a Hubel and Wiesel or the later
work of Jones and Palmer) of types of feature extraction that might be useful.
Quite often after classification, questions are asked about which features caused a
particular decision to be made. That is, the question of why a particular region of a
photograph was called President Clinton and another called Ross Perot. It's not the shoes.
It's got to be the ears! A related question is: of the many features that may have been
suggested as useful for a given problem which ones are the most important ones for the
task of interest? The answer to this question is often used to reduce the set of feature
measurements (vector) to a smaller dimension. This is critical in applications where there
are only a limited amount of training data available. To reduce the feature vector, the
most common statistical and trial-and-error techniques have been augmented with neural
feature saliency techniques. Conventional statistical correlation ideas are the most
common technique to find how features are related. The discovery of nonobvious
relationships between features may be one of the great contributions of neural networks.
One of the early applications of neural networks was in loan analysis. The data on the
application for the loan were fed into a neural network and the network that had been
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trained on historical data on loan defaults would predict whether you would default. For
litigation reasons the users of such networks had to be able to determine the application
information that the network considered to be the indicator of you eventually defaulting.
There also currently exists artificial neural network systems that monitor credit card
transactions to detect fraud. They are trained on historical transaction data and analyze
current transactions to detect fraudulent transactions.
As a side note, using the biological insight a good set of candidate features can
often be found. In the application of speaker identification, measurements of the
processing of the pinna and frequency extraction as a function of distance along the
cochlea have resulted in models that have been demonstrated useful in sound localization
and speaker identification.
V Classification
Once the features that are to be used to decide whether a particular region of
interest requires further attention are extracted, they are submitted to the classification
stage. This is the area where neural techniques have proven to be most useful. The most
common neural techniques require an enormous amount of labeled data. Labeled data has
to be hand labeled by experts. It is the experience of these experts that the classification
step must learn to encode in the intemonnection weights. In the application of face
recognition, some expert must feed the network with images and tell the network the
identity of the face. Similarly, someone must identify the voice from a training recording
before the system can identify the person from a later recording.
It has been proven many times in the literature that the common neural techniques
perform as approxlmators of the Bayes optimal decision elements (minimum probability of
error). This allows the user to know that if correcdy engineered there are no first order
SLatistical techniques which will outperform the neural algorithms with respect to
accuracy. Even with this knowledge the comparison of the neural classification algorithrus
with statistical techniques such as regression or quadratic discriminant function analysis is
useful to ensure that the neural technique is correctly engineered.
VI Future Work
The most important future area of research is in field test and demonstration.
Large scale tests will determine whether anything useful will come out of the preliminary
exciting results. It will only be by statistically significant improvement in real world
applications such as crime fighting that this technology will be proven.
Fundamental work on generalization predictions is also necessary. The question is
how much datawill be required in a given application to allow the system to be fielded
with some confidence on how well it will perform. How much shrinkage should be
expected from the acmwacy rate seen in training to the rate that is expected in the real
world.
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The combination of neural with fuzzy and expert system techniques will also play a
key role in driving these solutions to useful applications. Joint conferences, such as the
IEEE World Congress on Computational Intelligence, may allow a quick improvement in
this area.
One of the most interesting areas of research is in consciousness. Real brains, of
course, think about being real brains. The idea of self-awareness as a computation going
on within your brain is controversial but true. How does a piece of meat think about being
a piece of meat? Could meat ever understand how it does it? Why does human meat
seem to be different from that of other animals even though all mammalian brains are
constructed to the same basic plan using the same basic parts? There are fundamental
limits to the computational capability of the human brain. One way to see the limitations is
by the concept of Miller's magical number seven plus or minus two. The human brain is
limited to keeping track of about seven things. If keeping track of more than seven things
is required to build a stable world society then we have a problem. In the context of this
lecture if more "chunks" (more than seven) are required to understand self-awareness then
we will never understand how we do it. A puppy dog has fewer chunks than the seven.
How many does a chimp have? How can we measure the number of "chunks" for
nonverbal animals or ff they also can compute their own existence? Series of delay-non-
matching-to-sample tests may work here.
The illusion of self awareness is aided and abetted by a series of tricks and lies
perpetrated by the human sensory systems; the world is not quite the way it looks, not at
all the way it sounds, and the sense of the flow of time is a total confabulation which runs
about 200 milliseconds behind real time. The purpose of the brain is to construct as
accurate a model of the world as it can given the inevitable limitations of being made out
of meat. The results, though, are really amazing; we live inside our own private bags of
life which are equipped with a seemingly high fidelity stereo sound system, a 3-
dimensional movie display and complete cognizance of touch and smell. We have an
enormous content-addressable memory and can keep track of about seven things
simultaneously. We can manipulate arbitrary symbols and create the illusion that we are
aware of our own existence (and thus compute that it will someday end). Some of the
neural hardware forming the sensory systems was described in this lecture but a complete
description of how it all works does not exist nor is there any reason to imagine that a
human brain could understand k if it did.
VII Conclusions
It has been shown in several areas that artificial neural networks can make a
significant impact in fighting crime. The biometric authentication systems are being
fielded. The application of neural technology to other crime-related problems is
necessary. This will require a joint effort between experts in the law enforcement area
with signal processing people. Participation at the professional meetings of each group by
the other is critical
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TECHNICAL REPORT STANDARD TITLE PAGE
1. Report No. "94-10 2. Government Accession No.
4. Title and Subtitle
Decade of .Ueural Networks: Practica]
_or,lications and Prospects
7. Author(s) _.'
Sabrlna,_Kemeny
9. Per_rming Organization Name and Address
JET PROPULSION LABORATORY
California Institute of Technology
4800 Oak Grove Drive
Pasadena, California 91109
12. Sponsoring Agency Name and Addre_
NATIONAL AERONAUTICS AND SPACE ADMINISTRATION
Washington, D.C. 20546
3. Recipient's Catalog No.
5. Report Date
May Ii, 1994
6. Performing Organization Code
8. Performing Organization Red'oft No.
10. Work Unit No.
11. Contract or Grant No.
NAS 7-918
13. Type of Report and Period Covered
14. Sponsoring Agency.C_0_e
RP 23_ 0001 PX-_-II-O0-0
15. Supplementary Notes
16. A_tract
The Jet Prooulslon Laboratory Neural Network Workshop, sponsored
by NASA and DoD, brings together sponsoring agencies, active
researchers, and the user community to formulate a vision for the
next decade of neural network research and application prospects.
While the speed and computing power of microprocessors continue to
grow at an ever-lncreasing paee, the demand to intelligently and
adaptively deal with the complex, fuzzy, and often Ill-defined
world around us remains to a large extent unaddressed. Powerful,
highly parallel computing paradigms such as neural networks promise
to have a major impact in addressing these needs. Papers in the
workshop proceedings highlight benefits of neural networks _n
real-world applications compared to conventional computing
techniques. Topics include fault diagnosis, pattern recognition,
and multiparameter optimization.
17. Key Wor_ _elec_d by Author))
Electronlcs and Electrical
Engineering, _¢athematical and
Computer Sciences, Computer
Systems, Information Theory
18. Distribution Statement
19. Security Clms|f. _f thls report)
Unclassified
_. Security Clms|f. (ofthis _ge)
Unclassified
21. No. of Pages
235
22. Price
JPL 0184 R 9183 _"



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