The R&D Tax Aspects of Computer Enabled Human Identification



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        As more and more of our daily activities are conducted using computers and mobile devices, the need for failsafe identification and security protocols becomes increasingly important. In recent years, as computer processing power has continued to increase at a pace never before seen, development of new security technologies and methodologies has seen tremendous growth.

        Among these new technologies is the field of human identification by computers. As a result, the use of biometric data to identify people has become increasingly complex and in turn, increasingly valuable to many industries. From international and government security protocols to cell phone applications, the uses are as diverse as we can imagine. Cell phones can now identify your voice, your fingerprint and even your facial features. Biometric identification techniques once reserved for science fiction novels and movies are now commonplace in offices and even schools.

        Additionally, as a result of the proliferation of biometric data, the cryptographic community has been called to action as never before to create new ways to increase security of data in all phases of the security transaction; acquisition, transmission and storage.

        In the absence of this essential part of the overall security model, forgery will undoubtedly follow.

        Much of the work done in these emerging technologies may be supported by federal and state R&D tax credits.



The Research & Development Tax Credit

        Enacted in 1981, the federal Research and Development (R&D) Tax Credit allows a credit of up to 13% of eligible spending for new and improved products and processes. Qualified research must meet the following four criteria:

  • New or improved products, processes, or software
  • Technological in nature
  • Elimination of uncertainty
  • Process of experimentation

        Eligible costs include employee wages, cost of supplies, cost of testing, contract research expenses, and costs associated with developing a patent. On January 2, 2013, President Obama signed the bill extending the R&D Tax Credit for 2012 and 2013 tax years.



Background

        Computer enabled human recognition using biometrics is a rapidly developing area of technology. Biometrics essentially refers to a biological or behavioral characteristic that can be measured and then used for automated recognition.

        One of the biggest uses of biometrics is in the security arena. The U.S. Department of Homeland Security has spent over $133 million on biometrics since 2003 and the Defense Department is predicted to spend $3.5 billion on this technology between 2007-2015.

        In terms of security, in the most recent past, recognition (or authentication) was primarily made by one of two methods. It was either accomplished by evaluating something a person had with them (i.e. a passport or driver's license) or by evaluating something the person knew (i.e. PIN number or password). But with the evolving need for better and more sophisticated security methods coupled with the availability of increasingly more powerful computers, biometrics has become an area of intense interest to governments and private companies alike.

        Some of the more commonly known areas of human recognition using biometrics are: fingerprint recognition, facial recognition, voice recognition, and iris recognition. The process of recognition and authentication involves five basic steps:

  1. Sensor - collects data and converts the information to a digital format.
  2. Signal processing algorithms - performs quality control activities and develops the biometric template.
  3. Data storage - keeps information that new biometric templates will be compared to.
  4. Matching algorithm - compares the new biometric template to one or more templates in data storage.
  5. Decision process - uses the results from the matching component to make a system-level decision (either automated or human-assisted).

        Human recognition technology is currently used to allow access to buildings, computer files, and protected areas - both physical and virtual. It can be used to clock in and clock out of a workplace and identify the owner of an account prior to an in-person or over the phone transaction.

        The Indian government has recently started a massive identification program to obtain the fingerprints and iris scans of its 1.2 billion people. Country leaders say that this program will help ensure that government welfare spending reaches the right people and will allow millions of Indians in need to access services like banking for the first time.

        As hardware and software capabilities increase and security requirements become more complex, human recognition technology will continue to garner attention and R&D spending.

        In addition to security, these technologies are being used in a multitude of diverse industries. Two companies involved in this field, Emotient and iMotions recently announced a newly integrated platform that combines facial expression recognition and analysis, eye tracking, EEG, and GSR technologies. The combined solution is designed for use in a variety of applications including usability research, market research, neuro-gaming, and academic/scientific research.

        The medical field stands to benefit from biometric developments as well. Ottawa-based Autonomous ID has partnered with Carnegie Mellon University and has invested $1.5 million a year to establish a Pedo-Biometrics Research and Identity Automation Lab. Scientists have known for years that feet, as well as gait, are unique to each person. Sensors placed in the soles of shoes can check the pressure of the feet, monitor the gait, and create a master file that identifies each person. In the future, monitoring aspects of the foot may also prevent or assist in the diagnosis of diseases such as diabetes, Parkinson's and dementia.



Fingerprint Recognition

        Fingerprint recognition has been around since the 1800s and there is a very defined listing of fingerprint characteristics that can be obtained from an individual and subsequently compared to a stored image with acceptable levels of accuracy. Even so, the technology used to quantify fingerprint characteristics and compare them to existing data continues to improve and is being successfully implemented in many different situations around the world.

        In Apple's newest cell phones, the scanner looks beneath the outer layer of skin. Apple says this increases security because images of fingerprints (or detached fingers as we have seen in the movies) will not pass muster with the new technology.

        Michigan State University's Biometrics Research Group is working on several projects involving fingerprint and facial recognition and security of biometric template data. Here is a partial list of some of the current projects:

  • Biometric Template Security
  • Face Recognition at a Distance
  • Fingerprint Alteration
  • Heterogeneous Face Recognition
  • Latent Fingerprint Matching
  • Match & Retrieval of Images Based on Facial Marks
  • Extended Features in Fingerprints
  • Face Recognition in Low Quality Video
  • Fingerprint Reconstruction From Minutiae
  • Latent Fingerprint Enhancement
  • Latent Palmprint Matching
  • Models for Age Invariant Face Recognition



Facial Recognition

        Since September 11th, facial recognition technology has experienced exponential growth. Security cameras are increasingly omnipresent and continue to capture more images than a human could ever expect to review and evaluate. But computers can scan images much more rapidly than humans; they just need to know how to scan and what to look for. The answers to these deceptively simple questions will only be found through improved technology.

        Facial recognition has been used successfully when comparing a known image to a known template. For example, a computer can evaluate that Mary Smith is, in fact, Mary Smith when she requires entry to her office building. But successful use of technology that can compare current single images to large numbers of stored templates to obtain a match or identify certain people within a crowd is still under development and there is not one particular standard universally used in gathering and comparing facial features.

        Using facial recognition in crowds is of particular interest to governmental entities. The federal government is making progress on developing a surveillance system that would pair computers with video cameras to scan crowds and automatically identify people by their faces. The Department of Homeland Security tested a crowd-scanning project called the Biometric Optical Surveillance System (BOSS) last fall. Although the system is not ready for use, researchers say they are making significant advances.

        Facebook has recently started using facial recognition in their toolkit. When a person uploads photos to the site, the "Tag Suggestions" feature uses facial recognition to identify that user's friends in those photos and automatically suggests name tags for them.

        SceneTap, a new app for smart phones, uses cameras with facial detection software to scout bar scenes. Without identifying specific bar patrons, it posts information like the average age of a crowd and the ratio of men to women, helping bar-hoppers decide where to go. More than 50 bars in Chicago participate.

        As SceneTap suggests, techniques like facial detection, which perceives human faces but does not identify specific individuals, and facial recognition, which does identify individuals, are poised to become the next big thing for personalized marketing and smart phones.

        Another example of how this technology is spreading is Immersive Labs, a Manhattan company that has developed software for digital billboards using cameras to gauge the age range, sex and attention level of a passer-by. The smart signs deliver ads based on consumer's demographics. In other words, the system is smart enough to display, say, a Gillette ad to a male passer-by rather than an ad for feminine products.



Voice and Speech Recognition

        Voice and speech recognition are often used interchangeably but they are not really the same thing. Voice recognition is the process of recognizing the voice of the speaker; like recognizing a fingerprint. Speech recognition is the process of understanding what a person is actually saying; this involves the use of artificial intelligence software. Both technologies are tending to co-evolve and both are experiencing tremendous growth; presenting opportunities for research and development.

        The speaker recognition process relies on features influenced by both the physical structure of an individual's vocal tract and the individual's behavioral characteristics. Voice recognition is different from some other biometric methods in that speech samples are captured dynamically or over a period of time, such as a few seconds. Analysis occurs on a model in which changes over time are monitored, which is similar to other behavioral biometrics such as dynamic signature, gait, and keystroke recognition.

        A recent New York Times article compared the speech recognition capabilities of two popular cell phones. These capabilities can be categorized into three different functions. There's dictation, where the phone converts speech to text; commands, where you operate the phone by talking; and internet information searches. There are vast differences among the successes of the three. This article illustrates the potential for more research and development opportunities to improve speech recognition and artificial intelligence technologies. This is just one area of innovation in the cell phone industry; mobile devices present several diverse opportunities for R&D.

        Carnegie Mellon University is the developer of CMU Sphinx, an open source group of speech recognition and processing software products. Carnegie Mellon continues to be an active participant in the development of speech and language recognition innovations. Here is a partial listing of currently active projects at Carnegie Mellon:

  • SPHINX Group
  • The Ravenclaw-Olympus: dialog system framework developed as a successor of the CMU Communicator architecture
  • The Let's Go project: a spoken dialog system for the general public
  • Robust Recognition: automatic speech recognition in adverse environments
  • The FestVox Project: building synthetic voices
  • The Fluency Project: using speech for language learning
  • The Language Technologies Institute
  • DIPLOMAT: rapid-deployment speech to speech machine translation
  • KANT Machine Translation Project (knowledge-based, accurate translation for technical documentation)
  • PANGLOSS: multi-engine Spanish-to-English machine translation system
  • Interactive Systems Laboratories
  • The JANUS Project: a speech-to-speech translation project for spontaneous speech
  • VODIS: Voice Operated Driving Information System



R&D To Address Security and Privacy Concerns

        Not surprisingly, as technology improves, privacy issues have become an increasing concern. Although fingerprinting for security purposes has been used for years, it and the use of other biometric identification data becomes more commonplace and people are concerned, rightly so, that their biometric data is being stored on the internet or in company databases.

        In order to address these concerns, privacy laws will need to be expanded to adapt to the new technologies. But improved technology will become necessary as well - presenting additional opportunities for R&D in this arena. Methods of storing and encrypting biometric data will need to be improved. Recently, Fujitsu Laboratories Ltd. announced development of the world's first homomorphic encryption technology that enables statistical calculations and biometric authentication to be performed on encrypted data - without having to decrypt it - at high speeds.



Conclusion

        Human identification using biometrics is a rapidly emerging field with tremendous opportunities for growth and innovation. This innovation may be supported by federal and state research & development tax credits.

Article Citation List

   


Authors

Charles R Goulding Attorney/CPA, is the President of R&D Tax Savers.

Jacob Goldman is the VP of Operations at R&D Tax Savers.

Lynn Bertrand is a Tax Analyst with R&D Tax Savers.


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