| Рынок биометрической генерации пароля |
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Summary. Invention of biometric password generation targets market of password management. Its advantage over existing technologies is on-demand generated and unique password. Any technology may be regarded as competing if it does the same. Currently some technologies can not be used for password generation, namely voice, face, odor recognition. Passwords are expected to remain one of the most popular security measures and invention of new such measures may unlikely impact use of passwords. Our major assumption is that password protection will remain the most popular security measure. We do not compete with other security techniques, but just improve use of existing one – use of password management. Anyway, to answer whether existing or new technologies will impact our invention, we need to discuss its areas of aplication and how it’s supposed to be realized. 1. Application 2. Technology 1. Application of our invention is simple and straigntforvard: to generate paswords and ensure their security. Necessity of password generations is apparent for anyone using more than one password, either for banking, mail or administration. To store password is obviously insecure, whether in notepad or bulletproof safe. On demand generation is the most secure way of password creation and can be commercialized if there’ll be technology to implement it. Presented invention claims to offer such technology using fingerprints.  Any other biometric technology may impact our market positioning if it’s capable to do the same, so there is a question: which technology can do that. 2. There are no “new” biometric techniques, since inherent features of a human remain unchanged for his traceable history. Nevertheless, with technology development, new advances appear on the market and bring fortune to their inventors. The recent example is a group of Russian students, who offered device for face recognition and attracted few million investment from Japan venture fund. To generate password, biometric identifier should be permanent, distinctive and have high performance; for ease of use it must be universal and collectable. Fingerprints are basically the best choice for it. Fingerprint technology is also a market leader in biometric technologies and we present some comments below why ones are better than others. [1,2,3] • DNA: DeoxyriboNucleic Acid (DNA) is the one-dimensional ultimate unique code for one's individuality, except for the fact that identical twins have identical DNA patterns. It is, however, currently used mostly in the context of forensic applications for person recognition. • Face: The face is one of the most acceptable biometrics because it is one of the most common methods of recognition that humans use in their visual interactions. However, there are some tradeoffs in its use. Namely, the user is required to be presented in good light and to hold still as much as possible. Also, because face biometrics being passive, there can be concerns over spoofing and privacy. • Facial, hand, and hand vein infrared thermograms: The pattern of heat radiated by the human body is a characteristic of each individual body and can be captured by an infrared camera in an unobtrusive way much like a regular (visible spectrum) photo- photograph. The technology could be used for covert recognition and could distinguish between identical twins. A thermogram-based system is non-contact and non-invasive but sensing challenges in uncontrolled environments, where heat-emanating surf1aces in the vicinity of the body, such as, room heaters and vehicle exhaust pipes, may drastically affect the image acquisition phase. A related technology using near-infrared imaging is used to scan the back of a clenched fist to determine hand vein structure. Infrared sensors are prohibitively expensive which is a factor inhibiting widespread use of the thermograms. • Gait: Gait is the peculiar way one walks and is a complex spatio-temporal biometric. Gait is not supposed to be very distinctive, but is sufficiently characteristic to allow verification in some low-security applications. • Hand and finger geometry: Some features related to a human hand (e.g., length of fingers) are relatively invariant and peculiar (although not very distinctive) to an individual. The representational requirements of the hand are very small (nine bytes in one of the commercially available products), which is an attractive feature for bandwidth- and memory-limited systems. Due to its limited distinctiveness, hand geometry-based systems are typically used for verification and do not scale well for identification applications. • Iris: Visual texture of the human iris is determined by the chaotic morphogenetic processes during embryonic development and is posited to be distinctive for each person and each eye (Daugman, 1999a). Iris identification technology is a tremendously accurate biometrics. Ease of use and system integration has not traditionally been strong points with iris scanning devices, but people can expect improvements in these areas as new products emerge. • Odor: It is known that each object exudes an odor that is characteristic of its chemical composition and could be used for distinguishing various objects. It is not clear if the invariance in the body odor could be detected despite deodorant smells and varying chemical composition of the surrounding environment.  • Retinal scan: The retinal vasculature is rich in structure and is supposed to be a characteristic of each individual and each eye. It is claimed to be the most secure biometric since it is not easy to change or replicate the retinal vasculature The image acquisition involves cooperation of the subject, entails contact with the eyepiece, and requires a conscious effort on the part of the user. All these factors adversely affect public acceptability of retinal biometrics. Retinal vasculature can reveal some medical conditions (e.g., hypertension), which is another factor standing in the way of public acceptance of retinal scan-based biometrics. • Signature: The way a person signs his name is known to be a characteristic of that individual. Signatures are a behavioral biometric that change over a period of time and are influenced by physical and emotional conditions of the signatories. Signatures of some people vary a lot: even successive impressions of their signature are significantly different. • Voice: Voice capture is unobtrusive and voice print is an acceptable biometric in almost all societies. Voice may be the only feasible biometric in applications requiring person recognition over a telephone. Voice is not expected to be sufficiently distinctive to permit identification of an individual from a large database of identities. Moreover, a voice signal available for recognition is typically degraded in quality by the microphone, communication channel, and digitizer characteristics. Voice is also affected by a person's health (e.g., cold), stress, emotions, and so on. Besides, some people seem to be extraordinarily skilled in mimicking others. However, voice scanning may be integrated with finger-scan technology. Because many people see finger scanning as a higher form of authentication, voice biometrics will most likely be relegated to replacing or enhancing personal identification numbers (PINs), passwords or account names. • Finger-scan biometrics is based on the distinctive characteristics of a human fingerprint. If appropriate precautions are followed, the result is a very accurate means of authentication. It is not surprising that the workstation access application area seems to be based almost exclusively on fingerprints, due to the relatively low cost, small size and ease of integration of fingerprint authentication devices. As it’s clear from the above review, biometric techniques are different in applicability, use of use, acceptability, etc. It’s unlikely that advantages in some technique will change market share of others, especially of the most popular ones, like fingerprint or face recognition. Any successful combination of biometric techniques, however, is capable to create own market niche. The examples can be password and voice identification, fingerprint password generation. The market. Biometric market now worth $3.4 billion with annual growth of 29.1% ; this predominantly include finger scan technology (59%), facial and iris scanning (13%), keystroke (0.4%) and signature scans (2.7%). Proposed invention targets end-users, small, medium-size companies and widespread information systems which need: - Convenient, cost effective and secure solution for personal passwords storage, i.e. pocket USB-password generators; - Flexibility in installation of access control devices: i.e. biometric scanners and password generators in university labs and libraries or subscription services of internet media. Since security concerns in the modern world are constantly growing, biometric password generation is capable to gain at least 3% of total biometric market. We plan to sell product through chains of retailers in Europe (Mediamarkt, Eldorado, Foxtrot, etc) and USA (Radiojack, Wallmart, etc). This gives us less control for payment collection, prices and merchandising, but provides faster consumer market penetration. We also plan internet sales and have adequate knowledge, experience and resources to create, promote and maintain php-based (Joomla) internet shop. Our sales price was estimated on below market level (similar devices for 80Euro, see prices in attachment) and using cost-based calculations: spare parts for our products will constitute 50% costs (sensor and processor for 30Euro) and the remaining costs will be product assembling and processor programming. in Estonia. Preliminary headcount is 10 employees, producing 11000 devices per month. Sources 1. Davide Maltoni , Dario Maio , Anil K. Jain , Salil Prabhakar , Handbook of Fingerprint Recognition 2. Biometrics for Network Security By Paul Reid 3. David Zhang Biometric Image Discrimination Technologies Источники: 1. Davide Maltoni , Dario Maio , Anil K. Jain , Salil Prabhakar , Handbook of Fingerprint Recognition 2. Biometrics for Network Security By Paul Reid 3. David Zhang Biometric Image Discrimination Technologies |
