WO2015119520A1

WO2015119520A1 - The method and decision system of personal characteristics acquisition especially in biometrical authorisation systems

Info

Publication number: WO2015119520A1
Application number: PCT/PL2015/000014
Authority: WO
Inventors: Michał WALUS; Krzysztof BERNACKI
Original assignee: Walus Michał; Bernacki Krzysztof
Priority date: 2014-02-05
Filing date: 2015-02-05
Publication date: 2015-08-13
Also published as: PL407047A1

Abstract

The method of personal characteristics acquisition especially for biometrical authorization systems, using matrixes of diodes, light focusing and dispersing elements and matrixes of cameras based on the difference of absorption spectra for hemoglobin and other organism tissues, including multibiometrical attitude of one or many biometrical modalities in acquisition process, consists in two stages: in the first one (acquisition level) at least one raw biometrical image is being captured preferably presenting the vascular system, understood as the difference in absorption spectra of hemoglobin and other organism tissues, lightening the object with matrixes of diodes and capturing using matrixes of cameras with filters; where in the second stage obtained images are being quality evaluated by one or multithreading procedure preferable using criterial functions evaluating the quality of each of the images; if the quality of obtained image, appointed by quality evaluation and / or by the criterial function is above the required value, the image is being under further analysis; if the quality of the image, appointed by the criterial functions is below the required value, parameters of matrix of diodes elements are being sequentially changed and / or in a complete way, and operations from the first level are being repeated for new parameterization, where all steps are being repeated automatically creating the mechanism of feedback loop, repeatedly until the required value exceeds the threshold for one of the images during the quality evaluation process.

Description

The method and decision system of personal characteristics acquisition

especially in biometrical authorisation systems.

The subject of invention is the method and decision system of personal characteristics acquisition especially in biometrical authorisation systems also called AICO.

Individual physical and anatomical features are successfully used in biometrical identification and verification systems. In systems of this type, the access to the infrastructure resources is based on determination of correlations and similarities of biometrical features appearing in processed images which are the subject of acquisition process.

Acquisition of biometrical samples is fast and non-invasive process possible to complete without any physical contact between the hand (limb) and the device. Vascular system does not have any negative community impression, compared to other physical biometrical features like fingerprints which are used in criminology. Vascular pattern is generally not visible in the range of visible light, because of its under skin location. Acquisition and use of it in the biometrical system is practically not possible without the cooperation of its owner. Another advantage is its insensitiveness to external factors. Moreover, multibiometrical approach is additional factor increasing the safety, what influence the decrease of false acceptance and false rejection coefficients.

Currently known and used biometrical control systems are imperfect. In all known biometrical patterns acquisition devices quality assurance is obtained as a set of irreversible image transformations. A disadvantage of approach is relatively large computational power required for central unit module which process and match all information's obtained from acquisition devices.

Previous solutions focused on realization of necessary computations without system efficiency manners or time needed to match the patterns. In any research or known patents the issue related to multithread biometrical data processing where not been deepen. There was no research on LED diodes, widely used in acquisition devices, to verify which wavelengths in the range of near infrared is appropriate for vascular system data acquisition, or which wavelengths in multibiometrical approach maximize the biometric system efficiency. The decision systems with feedback loop also were not been proposed.

Identification and verification of human identity uses individual and unique (for each being) biometrical features. Till now the most common biometrical features allowed the recognition of fingerprints, iris, voice (way of speaking, base frequency, nose sound, cadence, words elongation and others), face (geometry and special features), hand geometry, signature verification etc. Individual and unique personal features which require more efforts in analysis, processing and identity verification are recognition based on retina or DNA, skin gloss, lips movement or body smell. With new technologies development more sophisticated and subtle processing methods are used. Before the new solution is introduced to wide range, variety of tests are being performed, to verify its usefulness, reliability and operation in biometrical authorization process.

The possibility of vascular system usability in personal identification and verification is being researched to apply this individual feature to the canon of biometrical modalities used in the process of access granting. Vascular system pattern recognition is noninvasive, reliable technique of individuals identification and verification. The vascular system techniques fit the actual market needs, which requires the common safety standard of assets protection.

Each biometrical feature can be treat as stable if satisfies the following properties: versality - each person should have given biometrical feature, uniqueness - no two people should have the same biometrical feature, durability - constant in time, measurability - it is possible to measure it with (practical) device, and acceptability - no resistance from users to the measurement of particular biometry. The advantage of using the vascular pattern in personal identification or verification is insensitiveness to external factors. It is anatomical and physiological feature, which satisfies all criteria's of biometrical stability. There are many methods of positioning the lighting and capturing elements used for image acquisition. Existing solutions can be classified to one of three groups: where lighting and capturing elements are on two sides of the finger, where they are on the same side and where there are perpendicular to the vasculature.

Vascular system is not visible by human eye, because it is around 2-4 mm under the surface of the skin, what increases the safety of the method in the contexts of fragile resources protection. Location of vascular system does not allow creation of artefacts imitating the human hand or it part. The acquisition of vasculature is almost impossible without the cooperation of its owner.

From Polish patent PL 195944 there is known the solution of humans identification based on fingerprint biometrical feature. The patent describes the method and device which consist the central management-steering-execution integrated unit with lack of the possibility to distract its functionalities to peripherals. The solution describes light sources (in the range of visible light, point source, not constructed as the matrix of known characteristic light sources, steered individually or in group), polarization filter and camera (not considering the possibility of advance cameras set ups steered using fixed schemes, without possibility of parameters changes). The subject of this patent is the method and device enabling people identification using fingerprints and patterns of under-skin tissues using wavelengths in the range of visible light (nor based on obtaining, exploring, interpreting and analyzing the difference in absorption spectra of hemoglobin for different light spectra). The solution is characterized by contactless registration in optical way. There are used additional light elements with polarization filters of the way of lightening beams. Sources of light are switched on and off one after each other, in order to capture the image of skin and under-skin tissues. Patterns of skin and under-skin tissues are being registered each time with one camera. To interpret fingerprints the finger skin pattern recognition algorithms are used, which do not use criterial functions to evaluate the quality of the image. As the light source the diode laser is used and / or metal surface lamp bulb. The device cover is made partially or entirely of metal, so it can take no intentional function of antenna, what could cause the electromagnetic discharges and affect negatively the device or people using it.

It must be concluded that device is sensitive for frauds attacks because of lack of protection mechanisms to detect the artificial finger or human hand fraudulent attempts. Light steering methods or hardware based quality improvement techniques were not been used. The solution does not allow the multibiometrical authorization approach, safe mode module, continuity of work module or safety zones. No electromagnetic field or gas sensors were considered which warn about the possibility of not authorized or improper use. The solution does not provide the possibility of remote steering or controlling the device.

From American patent description US 2013/0329031 there is known equipment to personal authentication by personal features consisting of acquisition device, visualization device, processing the image unit, mirror system and light sources. The device is additionally equipped with acquainted biometrical features registering system and system to compare similarities between images. Steering of light sources is sequential (source is switch on or off). Biometrical pattern creation is a fusion of images acquired for different light sources switched sequentially. The unit comparing biometrical patterns is binomial - it allows comparison of acquired image with the images from the device memory, it does not provide the functionality of local repository. The device is constructed in this way, that illuminating elements and camera are on the same device side. There are filters and a mirror system on the way of light beams, which minimizes the overlighting effect on the matrix on registering device. The device is used in acquisition of vascular system of fingers on hand. The illuminating elements which emit light are NIR LED diodes. There is no description of used characteristic light wavelengths. In the device it was provided from one to twenty light sources (individual luminescence diodes). The device in case of troubles with acquiring the image provide identification results on the bases of multimodal approach of finger vascular and fingerprints. There is lack of image quality assessment during the acquisition process. The device is equipped with diode light direction change system and the absorption-dispersion surface to minimize the effect of registration matrix overexposure.

The purpose of the invention is to develop new biometric data acquisition method with automatic decision-making rules and feedback loop mechanism for better authorization results of biometric system.

The method is characterized in that in the first stage at least one raw biometrical image is being captured preferably presenting the vascular system, understood as the difference in absorption spectra of hemoglobin and other organism tissues, lightening the object with matrixes of diodes and capturing using matrixes of cameras with filters; where in the second stage obtained images are being quality evaluated by one or multithreading procedure preferable using criterial functions evaluating the quality of each of the images; if the quality of obtained image, appointed by quality evaluation and / or by the criterial function is above the required value, the image is being under further analysis; if the quality of the image, appointed by the criterial functions is below the required value, parameters of matrix of diodes elements are being sequentially changed and / or in a complete way, and operations from the first level are being repeated for new parameterization, where all steps are being repeated automatically creating the mechanism of feedback loop, repeatedly until the required value exceeds the threshold for one of the images during the quality evaluation process.

The object is a hand or human limb.

The illuminated image is being divided for one preferable three regions - subregion of fingers, subregion of metacarpus, subregion of the whole hand, where each of them is being illuminated with different wavelengths and/or the same wavelengths. As criterial function the neural networks, Double Indicator technique and/or decision trees are being used.

The parameters of criterial functions are being set as predefined.

The whole unit is being controlled by informatic system and/or biometrical system and/or decision system with feedback loop.

The image is two-dimensional and/or three dimensional and/or signal and/or other digital form. The illumination of the object is being done with wavelengths in the range: for fingers from 250 nm until 700 nm and/or from 840nm until lOOOnm and/or for metacarpus from 720nm and 900nm and/or the whole hand from lOOOnm until 1400nm.

The measurement of temperature is being done during the acquisition process, using the matrix of temperature sensors, where criterial functions include preferable additional requirements for required temperature values.

The measurement of the parameters of pressure, contact force, distance and spatial vectorization is being done during the acquisition process, using the matrix of sensors, where criterial functions include preferable additional conditions for required parameters of pressure, contact force, distance and spatial vectorization.

The measurement of concentration and level of the alcohol are being done during the acquisition process using the matrix of alcohol-detection sensors, where criterial functions include preferable additional conditions for required parameters of concentration and level of the alcohol.

The image after the quality evaluation is being deformed using transformation operations.

Before the first stage the verification of the object environmental parameter's is being done based on behavioral evaluation, defined preferable according to COBIT 5 standard, where negative evaluation is being reported and/or break the procedure.

Inter-territorial transitions are being created for separated regions areas and overlap.

For inter-territorial transitions of image the local analysis techniques are being used considering boundary conditions.

The image is multi-spectral two-dimensional and/or three dimensional with the information of one or more biometrical modalities.

Biometrical modalities of fingers, hands, wrists, hand geometry, limb^'s geometry, nails, lunula, distribution of hair follicles or skin structure are obtained during the acquisition process in macro- and microscope view in unimodal or multibiometrical manner of above mentioned modalities, where preferable the modalities are obtained in the first stage.

The decision system is characterized with that computational-decision unit consists of connected and cooperating with each other subsystems: power module, operating status sensors, sensors, safe mode module, image processing and analysis module, operating conditions stabilization module, controllers, programmable logic system, backup power module, classification-verification module, steering module, microprocessors, peripheral modules, executive modules, converters and cryptographic module, converters and cryptographic modules, communication modules, continuity of work module, memory module, local repository, visualization module and data compression module.

Subsystems of computational-decision unit are connected by communication interfaces.

Diode matrix are the electroluminescence diodes and / or sources of visible, ultraviolet and / or infrared light using works on the basis of spontaneous or stimulated emission phenomenon. Camera matrix consists of photosensitive, photoelectric elements, such as CCD matrix, CMOS matrix or camera with opto- and digital steering module.

Peripheral module of temperature sensor elements works for point as well as area temperature measurement.

Peripheral modules of sensors are the circuits for determination the object state.

Communication buses support transmission of data and messages between computational- decision unit modules and inter-modular transmission as well as biometrical pattern transmission to external repositories.

Temperature, pressure, humidity, electromagnetic field, gas and vibration sensors are used.

Peripheral module of sensors the temperature, pressure, distance, spatial vectorization, the presence and level of concentration of intoxicate substances (like alcohol or drugs) sensors are used.

Elements of diode matrix and/or elements of camera matrix are positioned with servomechanisms.

The power module is grid and/or battery and/or autonomously from its own renewable sources of energy powered.

Peripheral modules are: keyboard, microphone, microprocessor card reader and/or magnetic card reader, OCR scanner, NFC reader, QRcodes, PIN Pad's. Cryptographic module encrypt the connection and data transmitted by communication interfaces and/or communication buses.

Visualization module consists of printing devices, display devices and information devices.

The main idea of the solution is that for images acquisition there are used characteristic wavelengths and control of the process is maintain taking into account the diode and camera matrices parameters changes based on the criterial functions for image quality assessment and maximization the quality in decision procedure.

The significant advantage is securing the biometric system authorization process by using multifactor verification with security mechanisms which prevent artificial finger attacks. For acquisition there are used contact- and contactless methods. The light transmission and light reflection attitude is allowed. The focusing and dispersing inserts are placed on camera matrices. The hipoalergic, antiseptic and fire resistant materials are used. For changes of the spectra ranges the band pass and band stop filters are in place. The system is compatible with available in the market topologies of biometrical systems such as: central and local repositories and also match on card technique. There are used data transmission protocols for communicating the system with external biometrical patterns repositories. There is allowed the distortion image transforms transit to computational-decision unit to allow proper data transmission to the repository. The suitable diode matrix is taken depending on the biometric modality to acquire. In each diode matrix is allowed the usability of the different lighting sources with different wavelength spectrum characteristic. Each element related to the process of transmission, reading or writing is maintain with reciprocity certificates and in accordance with industry standards of cryptographic principles. Each non authorized attempt cause the system deactivation. There is resistance to external factors threatening the normal work conditions. Similar situation is for distortions and disturbances of electromagnetic field. Used transmission protocols are the secure protocols. Elements and areas of physical contact with human are isolated and separated from electronics and logical system parts. There are used techniques which do not allow the formation of potential difference which could impact the normal working conditions. The decision system cooperate with access control and vision systems and devices. The necessity of service inspection is alerted. The camera matrix is compatible with different color management systems. The computational-decision unit is compatible with available computer architectures. The image processing and analysis module allow the image quantization for purpose of ensuring the compatibility with worse quality images obtained and stored in the repository.

The advantages of the solution with respect to the decision system are also:

- the sensors are not touched by the users,

- the decision system can be places behind the protective wall,

- the system is design with respect to block and do not spread the nuclear, chemical or biological threats,

- there is used the innovative method of authorization based on the differences of light absorption by hemoglobin and other organism tissues, the multibiometrical approach, criterial functions and other innovative solutions proposed in the solution witch increase the security and resistance to frauds,

The invention is explained in examples and in embodiments figures, where:

Fig.1 present the decision system,

Fig.2 present the data flow diagram in decision system,

Fig.3 present the optimal wavelengths for the use in acquisition process,

Fig.4 present the basic version of the decision system,

Fig.5 present the version of the decision system with feedback loop,

Fig.6 present the version of the decision system with multiple feedback,

Fig.7 present the decision system in multibiometrical variant,

Fig.8 present different methods of diodes and camera matrices set ups,

Fig.9 present the inter-territorial transitions,

Fig.10 present the variant of acquisition module for two and/or three dimensional imaging. I. The method

In the first stage at least one raw biometrical image is being captured preferably presenting the vascular system, understood as the difference in absorption spectra of hemoglobin and other organism tissues, lightening the object with matrixes of diodes and capturing using matrixes of cameras with filters; where in the second stage obtained images are being quality evaluated by one or multithreading procedure preferable using criterial functions evaluating the quality of each of the images; if the quality of obtained image, appointed by quality evaluation and / or by the criterial function is above the required value, the image is being under further analysis; if the quality of the image, appointed by the criterial functions is below the required value, parameters of matrix of diodes elements are being sequentially changed and / or in a complete way, and operations from the first level are being repeated for new parameterization, where all steps are being repeated automatically creating the mechanism of feedback loop, repeatedly until the required value exceeds the threshold for one of the images during the quality evaluation process.

The object is a hand or human limb.

The illuminated image is being divided for one region.

The whole unit is being controlled by informatic system with feedback loop.

The image is in digital form.

The illumination of the object is being done with wavelengths in the range from 840nm until lOOOnm.

The image is multi-spectral two-dimensional and/or three dimensional with the information of biometrical modality.

Biometrical modalities of fingers are obtained during the acquisition process preferable obtained in the first stage. Example 1 (basic approach - only quality assessment. FIG.4)

- set up the intensity levels of 5 diode matrix to the value 50%

- set up the camera to autofocus,

- image 1 acquisition,

- reading the parameters based on co-occurrence matrix - quality assessment (contrast [0.75], correlation coefficient [0,73], image energy [0.18], homogeneity [0.12]) - the image meet the pre- requested conditions (the mechanism of feedback loop won't be used),

- the image 1 will be used by decision system.

Example 2 (basic approach + temperature measurement, quality assessment)

- set up the intensity levels of 5 diode matrix to the value 50%

- set up the camera to autofocus,

- image 1 acquisition,

- reading the parameters based on co-occurrence matrix - quality assessment (contrast [0.73], correlation coefficient [0,74], image energy [0.20], homogeneity [0.13]) - the image meet the pre- requested conditions,

- reading the object temperature,

- the temperature is in range 30-42 Celsius degrees - the image 1 will be used by decision system.

Example 3 (basic feedback loop, quality assessment. FIG.5)

- set up the parameter of minimal image quality requirements for criterial function to 2,

- set up the intensity levels of 7 diode matrices to the value 50%

- set up the camera to autofocus,

- image 1 acquisition,

- reading the parameters based on co-occurrence matrix - quality assessment (contrast [0.22], correlation coefficient [0,12], image energy [0.18], homogeneity [0.40]) - the image do not meet the pre- requested conditions,

- changing the set up of the intensity levels of first, third, fifth diode to the value 65%

- image 1 acquisition, - reading the parameters based on co-occurrence matrix - quality assessment (contrast [0.68], correlation coefficient [0,64], image energy [0.24], homogeneity [0.38]) - the image meet the pre- requested conditions,

- quality evaluation of the image 2 (as a reference to the image 1) - the method of Shannon entropy is used,

- the criterial function return the image 2, the image 2 has 23,5% better quality estimator than the image 1 (the result computed on the basis of Double Indicator method),

- the image 2 will be used by decision system.

Example 4 (3-times feedback loop. FIG.6)

- set up the parameter of minimal images compared by criterial function to 2,

- set up the intensity levels of 8 diode matrices to the value 45%

- set up the camera to autofocus,

- image 1 acquisition,

- reading the parameters based on co-occurrence matrix - quality assessment (contrast [0.22], correlation coefficient [0,12], image energy [0.18], homogeneity [0.40]) - the image meet the pre- requested conditions,

- changing the set up of the intensity levels of first, third, fifth diode to the value 75%

- image 2 acquisition,

- reading the parameters: brightness [40%], contrast [0.23], statistical moments [121 ,18,13,3,27,10,1 1], the number of pixels with intensity level in canal R (RGB model) between 40 and 84 [138487] - the image do not meet the pre- requested conditions,

- changing the set up of the intensity levels of first, third, fifth diode to the value 48%, the second and fourth to the value 79%,

- image 3 acquisition,

- reading the parameters - image histogram (quality assessment) in R and B channels - 13%, the area grater in the range 0-128 than in 128-255 range - the image meet the pre- requested conditions,

- criterial function (neural network) return image 1, image 1 has 1 1% greater quality estimator than the image 3,

- the image 3 will be used by decision system. Example 5 (multibiometrical approach. FIG.7).

- set up of used two biometric modalities:

1. the difference of absorption spectra for hemoglobin and other organism tissues,

2. the skin fold modality.

- two threads ( weights 60% and 40%).

- force of criterial functions usability (lack of predefined quality estimation),

- set up the number of images for criterial functions to 3

For 1 biometric modality:

- set up the four 5x5 diode matrices to random values (different wavelengths, the conditions to be meet: 42% - 940nm, 33% - 850nm, 12% - 730 nm, 8% - 1 l OOnm, 5% - 870nm)

For 2 biometric modality:

- set up the four 5x5 diode matrices (different wavelengths, the conditions to be meet: 70%> - 520 nm,

30% - for 480nm the random values)

- the 6 cameras set up to the autofocus mode,

- reading the parameters: temperature (34 degrees - in the range), gas (in the norm), humidity (55% - in range), electromagnetic field (in range), pressure (990 hPa - in the range),

- continuation in normal mode,

- acquisition the multispectral 3D image 1,

- the random set up of diodes intensities,

- change of the camera parameters,

- acquisition the multispectral 3D image 2,

- the random set up of diodes intensities,

- change of the camera parameters,

- acquisition the multispectral 3D image 3,

- criterial function return the image 2, the image 1 has 9% greater quality than the image 3. II. DECISION SYSTEM

The decision system of personal features acquisition consists of connected and cooperating one to each other elements, which is patterns acquisition unit (1) connected by communication buses (31) and communication interfaces (30) with computational-decision unit (3), where computational-decision unit (3) is connected with repository (2) using communication buses (31). Patterns acquisition unit (1) consists of the diode matrix (27), camera matrix (28). Computational-decision unit (3) consists of connected and cooperating with each other subsystems: power module (4), image processing and analysis module (8), steering modules (14), microprocessors (15), executive modules (17), converters (19), communication modules (21). Subsystems of computational-decision unit (3) are connected by communication interfaces (31). Diode matrixes (27) are electroluminescence diodes. Camera matrixes (28) consist of matrixes with light-sensitive photoelectric elements. Communication buses (31) are systems supporting the transmission of data, information's and communicates between particular modules of computational-decision unit (3), intermodular transmission in the system and biometrical patterns transmission from the system to external repositories (2, 40). Power module (4) is powered from grid.

Operation

Modular system of patterns acquisition unit (1) and computational-decision unit (3) with external repository (2) (database with biometrical patterns). Communication between particular modules is provided by communication buses (31). By communication buses (31) the data transmission and power to particular modules of patterns acquisition unit (1) and computational-decision unit (3) is provided. Data delivery is realized by internal and external communication interfaces (30). In the construction of the system model OSI was used - reference model of systems connectivity describing network communication structure, where data encapsulation process is being used for following layers: application, presentation, session, transport, network, data, physical. Used communication interfaces (30) have direct connection with particular layers, depending on needs of particular system modules. On the level of particular communication layers (according to above described model) communication buses (31) allow intermodular communication. Patterns acquisition unit (1) communicates with computational-decision unit (3) via external communication interfaces (30) and communication buses (31) of bidirectional transfer. Patterns acquisition unit (1) is equipped in sensors (6), which are under computational-decision unit (3) module management. There exists a possibility of images or biometrical patterns transfer from computational-decision unit (3) to repositories (2, 40) via communication buses (31). There is a possibility of image deformation transformations definition in order to image export from the system to external computer systems (including repositories (2)) providing the compatibility with local law regulations. In the process of individual personal features acquisition process diode matrices (27) are used as an illuminating element. In case of individual personal features acquisition of hand the illumination with characteristic wavelength values are used (41,42,43) FIG. 3. Communication interfaces (30) in case of patterns acquisition unit (1) control diode matrices (27) and camera matrices (28). Second integral part of the system is computational- decision unit (3), which is autonomous expert system, which emulates decision making process. As integral parts of computational-decision unit (3) are included: converters (19), cryptographic modules (20), operating status sensors (5), sensors (6), safe mode module (7), image processing and analysis module (8), operating conditions stabilization module (9), programmable logic system (1 1), power module (4), classification-verification module (13), controllers (10), microprocessors (15), peripheral modules (16), executive modules (17), communication modules

(21) , steering modules (14), data compression module (26), memory module (23), local repository (24) and visualization module (25). Cryptographic modules (20) generate safe cryptographic keys and to generate random numbers. They provide powerful multicomponent operation authentication - they connect certificate authentication and safe protocols encoding. They are directly connected with converters (19). Converters (19) converse signals in different abstraction layers. They enable signal electrical conversion (band pass and band stop filtering), conversion from electrical signals to logic signals. Conversion from analog signals to digital representation. It allows processing of signals with zero-one architecture and data gathering in local repository (24). Local repository (24) in case of lack of communication with external repository (2) of biometrical patterns takes the responsibility for image and biometric pattern storage (33), images and patterns obtained during lack of communication with external systems. In case of recovery of communication with external systems through continuity of work module

(22) , the connection is reestablished and attempt of data retransmission occurs. In local repository (24) information's about biometrical patterns (33) are stored. Memory module (23) has analogical functionality, but unlike local repository (24), there is being stored the image (32) which is the subject of acquisition. Use of memory module (23) or local repository (24) depends on chosen method and mode of cooperation with available topologies of biometrical systems. Sensors (6) are the source of data describing the work environment of the decision system (34) (temperature, humidity, pressure, electromagnetic field level and its disturbances, concentration of gases, vibrations etc.). State of work is signalized by visualization module (25) with particular consideration of actual state and presentation of chosen parameters inside computational- decision unit (3). This module is directly associated with operating conditions stabilization module (9), providing correct system operation though external environment changes. It is associated also with steering modules (14) which are decision unit in this process. Visualization module (25) is integrated with peripheral modules (16) (like printer used to print values of parameters). Communication module (21), which provides communication, consists of two submodules. External communication module is responsible for signalization of service-check need of the decision system (34), what is shown by operating status sensors (5). Module of internal communication is directly associated with cryptographic modules (20) and converters (19), which transfer information's inside the computational-decision unit (3). Communication module (21) is characterized by logic construction (hybrid model) linking module of internal and external communication. All elements of the decision system (34) are powered from integrated power system consisting of power module (4) and backup power module (12). Integrated power system enables the use of more than one independent power system, e.g. power from grid and renewable energy source. In case when safe mode module (7) signalizes power problems, steering modules (14) automatically switch or turn off troubled power source. Steering modules (14) with image processing and analysis module (8), programmable logic module (1 1 ) and classification and verification module (13) controls through executive modules the work of computational-decision unit (3) through implemented judgment mechanisms and criterial functions. Steering modules (14) have the adaptive functionality and possibility to work as expert system which has a possibility of reprogramming programmable logic systems (1 1) which are module of computational-decision unit (3). Programmable logic systems (1 1), microprocessors (15) and controllers (10) can take care of realization of dedicated tasks considering multithread approach in control and management of processes. Important elements of the system are peripheral modules (16) enabling cooperation with external systems, devices etc. Executive module (17) controls through external interfaces the camera matrices (28), which are the component of pattern acquisition unit (1). Data compression module (26) enables data compression - image compression and compression of patterns sent to repositories (2, 40). Diagram of data flow in computational-decision unit is shown on FIG. 2.

LIST OF DESIGNATIONS

1. Patterns Acquisition Unit

2. Repository

3. Computational-decision unit

4. Power module

5. Operating status sensors

6. Sensors

7. Safe mode module

8. Image processing and analysis module

9. Operating conditions stabilization module

10. Controllers

1 1. Programmable logic system

12. Backup power module

13. Classification-verification module

14. Steering modules

15. Microprocessors

16. Peripheral modules

17. Executive modules

18. Converter and cryptographic module

19. Converters

20. Cryptographic modules

21. Communication modules

22. Continuity of work module

3. Memory module

4. Local repository

25. Visualization module

6. Data compression module

7. Diode matrix 28. Camera matrix

29. Peripheral modules (sensors)

30. Communication interfaces

31. Communication buses

32. Image

33. Biometric pattern

34. Decision system

35. Parameters of peripheral devices and systems

36. Matrices parameters

37. Patterns Acquisition

38. Decision algorithm with criterial functions

39. Memory block

40. Internal or external repository

41. Used wavelengths for hand

42. Used wavelengths for fingers

43. Used wavelengths for metacarpus and wrist

44. Inter-territorial transitions

GLOSSARY

Acquisition is understood as the acquisition of individual characteristics.

Multibiometrical approach is understood as the usage of more than one individual physical or behavioral biometric feature.

Security protocol is understood as communication initiated by the biometric system user that uses an alternate path for access to infrastructure resources.

Infrastructure resource is understood as protected asset from unauthorized access, to which access may be granted as a result of a positive identity verification or identification in biometric system.

Alternate path is understood as the possibility to access the infrastructure resource after unsuccessful attempts of identity authorization.

Biometrical pattern is understood as extracted from the input raw image the mathematical- statistical feature which allow to authorize the access to the resource. Vascular pattern is understood as a difference of absorption spectra for oxygenated, deoxygenated hemoglobin and other organism tissues in specified wavelengths spectrum.

Safety/security zone is understood as the area close to the acquisition device / system in time of granting the access to the infrastructure resource where can be present only verified/identified in the biometric system person (during authorization).

Claims

The patent claims

1. The method of personal characteristics acquisition especially for biometrical authorization systems, using matrixes of diodes, light focusing and dispersing elements and matrixes of cameras based on the difference of absorption spectra for hemoglobin and other organism tissues, including multibiometrical attitude of one or many biometrical modalities in acquisition process, consists in two stages: in the first one (acquisition level) at least one raw biometrical image is being captured preferably presenting the vascular system, understood as the difference in absorption spectra of hemoglobin and other organism tissues, lightening the object with matrixes of diodes and capturing using matrixes of cameras with filters; where in the second stage obtained images are being quality evaluated by one or multithreading procedure preferable using criterial functions evaluating the quality of each of the images; if the quality of obtained image, appointed by quality evaluation and / or by the criterial function is above the required value, the image is being under further analysis; if the quality of the image, appointed by the criterial functions is below the required value, parameters of matrix of diodes elements are being sequentially changed and / or in a complete way, and operations from the first level are being repeated for new parameterization, where all steps are being repeated automatically creating the mechanism of feedback loop, repeatedly until the required value exceeds the threshold for one of the images during the quality evaluation process.

2. The method, according to claim 1, characterized in that the object is a hand or human limb.

3. The method, according to claims 1 and 2, characterized in that the illuminated image is being divided for one preferable three regions - subregion of fingers, subregion of metacarpus, subregion of the whole hand, where each of them is being illuminated with different wavelengths and/or the same wavelengths.

4. The method, according to claim 1, characterized in that as criterial function the neural networks, Double Indicator technique and/or decision trees are being used.

5. The method according to claim 4, where the parameters of criterial functions are being set as predefined.

6. The method according to claim 1, characterized in that the whole unit is being controlled by informatic system and/or biometrical system and/or decision system with feedback loop.

7. The method according to claim 1, characterized in that the image is two-dimensional and/or three dimensional and/or signal and/or other digital form.

8. The method according to claims 1, 2,3, characterized in that the illumination of the object is being done with wavelengths in the range: for fingers from 250 nm until 700 nra and/or from 840nm until lOOOnm and/or for metacarpus from 720nm and 900nm and/or the whole hand from lOOOnm until 1400nm.

9. The method according to claim 1, characterized in that the measurement of temperature is being done during the acquisition process, using the matrix of temperature sensors (29), where criterial functions include preferable additional requirements for required temperature values.

10. The method according to claim 1, characterized in that the measurement of the parameters of pressure, contact force, distance and spatial vectorization is being done during the acquisition process, using the matrix of sensors (29), where criterial functions include preferable additional conditions for required parameters of pressure, contact force, distance and spatial vectorization.

11. The method, according to claim 1 , characterized in that the measurement of concentration and level of the alcohol are being done during the acquisition process using the matrix of alcohol-detection sensors (29), where criterial functions include preferable additional conditions for required parameters of concentration and level of the alcohol.

12. The method according to claim 1, characterized in that the image after the quality evaluation is being deformed using transformation operations.

13. The method according to claim 1, characterized in that before the first stage the verification of the object environmental parameter's is being done based on behavioral evaluation, defined preferable according to COBIT 5 standard, where negative evaluation is being reported and/or break the procedure.

14. The method according to claims 1 and 3, characterized in that inter-territorial transitions (44) are being created for separated regions areas and overlap.

15. The method according to claim 14, characterized in that for inter- territorial transitions of image (44) the local analysis techniques are being used considering boundary conditions.

16. The method according to claims 1 and 15, characterized in that the image is multi-spectral two-dimensional and/or three dimensional with the information of one or more biometrical modalities.

17. The method according to claim 1, characterized in that biometrical modalities of fingers, hands, wrists, hand geometry, limb^'s geometry, nails, lunula, distribution of hair follicles or skin structure are obtained during the acquisition process in macro- and microscope view in unimodal or multibiometrical manner of above mentioned modalities, where preferable the modalities are obtained in the first stage.

18. The decision system of personal characteristics acquisition with data repository especially for biometrical authorization systems, is characterized with that it contains the collaborating elements connected one to another, which are the biometrical patterns acquisition unit (1) connected by communication buses (31) and / or communication interfaces (30) with computational-decision unit (3) where the computational-decision unit (3) is connected to repository (2) using communication buses (31) and / or communication interfaces (30).

19. The system, according to claim 18, characterized in that patterns acquisition unit (1) consists of the diode matrix (27), camera matrix (28) and peripheral modules (29) of sensors.

20. The system, according to claim 18, characterized in that computational-decision unit (3) consists of connected and cooperating with each other subsystems: power module (4), operating status sensors (5), sensors (6), safe mode module (7), image processing and analysis module (8), operating conditions stabilization module (9), controllers (10), programmable logic system (11), backup power module (12), classification-verification module (13), steering module (14), microprocessors (15), peripheral modules (16), executive modules (17), converters and cryptographic module (18), converters (19) and cryptographic modules (20), communication modules (21), continuity of work module (22), memory module (23), local repository (24), visualization module (25) and data compression module (26).

21. The system, according to claim 18, characterized in that subsystems of computational- decision unit (3) are connected by communication interfaces (30).

22. The system, according to claim 19, characterized in that diode matrix (27) are the electroluminescence diodes and / or sources of visible, ultraviolet and / or infrared light using works on the basis of spontaneous or stimulated emission phenomenon.

23. The system, according to claim 19, characterized in that camera matrix (28) consists of photosensitive, photoelectric elements, such as CCD matrix, CMOS matrix or camera with opto- and digital steering module.

24. The system, according to claim 19, characterized in that peripheral module (29) of temperature sensor elements works for point as well as area temperature measurement.

25. The system, according to claim 19, characterized in that peripheral modules of sensors (29) are the circuits for determination the object state.

26. The system, according to claim 18, characterized in that communication buses (31) support transmission of data and messages between computational-decision unit (3) modules and intermodular transmission as well as biometrical pattern transmission to external repositories (2, 40).

27. The system, according to claim 19, characterized in that temperature, pressure, humidity, electromagnetic field, gas and vibration sensors (6) are used.

28. The system according to claim 19, characterized in that peripheral module (29) of sensors the temperature, pressure, distance, spatial vectorization, the presence and level of concentration of intoxicate substances (like alcohol or drugs) sensors are used.

29. The system, according to claim 19, characterized in that, elements of diode matrix (27) and/or elements of camera matrix (28) are positioned with servomechanisms.

30. The system, according to claim 20, characterized in that the power module (4) is grid and/or battery and/or autonomously from its own renewable sources of energy powered.

31. The system according to claim 20, characterized in that peripheral modules (16) are: keyboard, microphone, microprocessor card reader and/or magnetic card reader, OCR scanner, NFC reader, QRcodes, PIN Pad's.

32. The system according to claim 20, characterized in that cryptographic module (20) encrypt the connection and data transmitted by communication interfaces (30) and/or communication buses (31).

33. The system, according to claim 20, characterized in that visualization module (25) consists of printing devices, display devices and information devices. _^