The goal of the Smart Gun Technology project is to eliminate the capability of an unauthorized user form firing a law officer`s firearm by implementing user-recognizing-and-authorizing (or {open_quotes}smart{close_quotes}) surety technologies. This project was funded by the National Institute of Justice. This report lists the findings and results of the project`s three primary objectives. First, to find and document the requirements for a smart firearm technology that law enforcement officers will value. Second, to investigate, evaluate, and prioritize technologies that meet the requirements for a law enforcement officer`s smart firearm. Third, to demonstrate and document the most promising technology`s usefulness in models of a smart firearm.

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Smart Gun Technology project. Final report

A technique for fmgerprint comparison based on template matching is presented. A digitised greyscale image is initially pre-processed, from which the template is derived. The use of "don't care" states in the template, which inhibit pixel comparisons, prevents environmental variations and noise from adversely affecting correlation results with subsequent images. The novelty of this approach is that although template matching is a mature method of pattern recognition, there are no reported successful attempts the solve the problem of fingerprint comparison using this technique. The fmgerprint reference comprises a set of sub-templates in order to overcome localised skin stretching. These are individually correlated with the processed binary image. Significant correlation scores of each of the sub-templates are posted in a voting area. After all the sub-templates have correlated with the image, this area is then polled for clusters of votes, whose density determines the success of the comparison. It is seen that pattern matching techniques are dependent.on the clarity of data they process, and a method for capturing fingerprint images of a consistently high quality is presented. A parallel template matching architecture comprising an array of 32 correlation cells is also presented. The array enables the simultaneous correlation of four sub-templates with eight areas of the image. This architecture makes use of industry standard byte wide random access memories (RAM) for storing the reference templates and the image. The algorithms that comprise the fingerprint comparison system are taken from concepts, through a stage of empirical development and extensive field trials, to an eventual compact and cost effective Very Large Scale Integration (VLSI) Application Specific Integrated Circuit (ASIC) based implementation.

Fingerprint comparison by template matching

As computerised storage and control of information is now a reality, it is increasingly necessary that personal identity verification be used as the automated method of access control to this information. Automatic facial recognition is now being viewed as an ideal solution to the problem of unobtrusive, high security, personal identity verification. However, few researchers have yet managed to produce a face recognition algorithm capable of performing successful recognition, without requiring substantial data storage for the personal information. This thesis reports the development of a feature and measurement based system of facial recognition, capable of storing the intrinsics of a facial image in a very small amount of data. The parameterisation of the face into its component characteristics is essential to both human and automated face recognition. Psychological and behavioural research has been reviewed in this thesis in an attempted to establish any key pointers, in human recognition, which can be exploited for use in an automated system. A number of different methods of automated facial recognition which perform facial parameterisation in a variety of different ways are discussed. In order to store the relevant characteristics and measurements about the face, the pertinent facial features must be precisely located from within the image data. A novel technique of Limited Feature Embedding, which locates the primary facial features with a minimum of computational load, has been successfully designed and implemented. The location process has been extended to isolate a number of other facial features. With regard to the earlier review, a new method of facial parameterisation has been devised. Incorporated in this feature set are local feature data and structural measurement information about the face. A probabilistic method of inter-person comparisons which facilitates recognition even in the presence of expressional and temporal changes, has been successfully implemented. Comprehensive results of this novel recognition technique are presented for a variety of different operating conditions. In identifying possible avenues of future research to come from this work, the hardware implementation of this algorithm has been considered. By detailed analysis of the proposed algorithm the first tentative steps have been taken in this direction.

Automatic facial recognition based on facial feature analysis

Biometrics, the measurement of ‘people‐based’ characteristics is rapidly becoming a major field of technology as an expert explains.

John R. Parks

Papers

Biometrics: the people sensors