Live finger detection device and detection method

Abstract

The present application provides a live fingerprint detection device and a live fingerprint detection method, wherein the live fingerprint detection device comprises a light source and an identification module, wherein the light source generates light emission to a to-be-detected fingerprint, wherein the identification module comprises at least one sensor and an optical assembly, the optical assembly is located in the optical path of the at least one sensor, and the reflected light of the to-be-detected fingerprint reaches the at least one sensor through the optical assembly, and the at least one sensor performs live fingerprint judgment based on the spectral information of the received reflected light.

Description

Technical Field

[0001] This invention relates to the field of fingerprint detection, and more particularly to a live fingerprint detection device and detection method. Background Technology

[0002] Various types of biometric systems are increasingly being used to provide enhanced security and / or improved user convenience. For example, fingerprint sensing systems, due to their small size, high performance, and widespread user acceptance, are widely used in various terminal devices, such as consumer smartphones. Currently, several fingerprint sensing systems are available on the market, such as those based on capacitive fingerprint modules and those based on optical fingerprint modules. While these types of fingerprint sensing systems can unlock devices, their application in mobile terminal fingerprint recognition allows criminals to steal user fingerprints and create fake fingerprints to bypass the user's security system. This actually increases the probability of fingerprint passwords being compromised on mobile terminals, posing a significant threat to the information security of mobile devices.

[0003] However, existing live fingerprint recognition solutions all have certain drawbacks. For example, capacitive modules have disadvantages such as poor environmental stability, low lifespan, and insufficient liveness detection capability, while optical modules usually do not have liveness detection capability. Therefore, there is an urgent need for a simple and reliable fingerprint recognition solution to achieve live fingerprint recognition. Summary of the Invention

[0004] A key advantage of this invention is that it provides a live fingerprint detection device and detection method, wherein the live fingerprint detection device is suitable for live detection, thereby improving the applicability of the fingerprint detection device.

[0005] Another advantage of the present invention is that it provides a live fingerprint detection device and detection method, wherein the live fingerprint detection device makes a liveness determination based on the spectral information reflected by the skin, thereby realizing the liveness detection of fingerprints and improving the accuracy of detection.

[0006] Another advantage of the present invention is that it provides a live fingerprint detection device and detection method, wherein the live fingerprint detection device determines the identification result of the object to be identified based on the comparison result of reference spectral response data and recognition spectral response data, which helps to improve the accuracy of fingerprint detection and identification.

[0007] Another advantage of the present invention is that it provides a live fingerprint detection device and detection method, wherein the live fingerprint detection device includes a light source and a recognition module, wherein the light source is disposed at or adjacent to the recognition module and is used to illuminate the fingerprint to be tested.

[0008] Another advantage of the present invention is that it provides a live fingerprint detection device and detection method, wherein the light source is disposed on the circuit board or frame of the recognition module, which is conducive to the miniaturization of the live fingerprint detection device.

[0009] Another advantage of the present invention is that it provides a live fingerprint detection device and detection method, wherein the live fingerprint detection device obtains raw data, i.e. light intensity information, performs image information correction and spectral information correction on the raw data respectively, and then uses fingerprint recognition algorithm and liveness algorithm respectively to compare the fingerprint image and spectral information with the corresponding information extracted during the recording to obtain the matching degree. When both matching degrees are higher than the threshold, the input verification is passed; otherwise, the output verification fails.

[0010] Another advantage of the present invention is that it provides a live fingerprint detection device and detection method, wherein the live fingerprint detection method includes image information correction and spectral information correction, including an image processing method with surrounding mean compensation (binning), which improves the accuracy of data detection through weighted averaging.

[0011] Another advantage of the present invention is that it provides a live fingerprint detection device and detection method, wherein the live fingerprint detection method further includes a liveness algorithm process, which extracts effective corrected spectral parameters (or spectral information) from the original data (light intensity information) after processing, forms a data group with the corresponding parameters of the entered data, and calculates the correlation coefficient R after linear fitting. When the correlation coefficient R is greater than the corresponding threshold, it is determined to be a live fingerprint; otherwise, it is determined to be a non-live fingerprint.

[0012] According to one aspect of the present invention, a live fingerprint detection device of the present invention, capable of achieving the aforementioned and other objects and advantages, comprises:

[0013] A light source, wherein the light generated by the light source is emitted onto the fingerprint to be tested; and

[0014] An identification module, wherein the identification module includes at least one sensor and an optical component, the optical component being located in the optical path of the at least one sensor, the reflected light of the fingerprint to be tested reaching the at least one sensor via the optical component, wherein the at least one sensor performs a live fingerprint determination based on the spectral information of the received reflected light.

[0015] According to one embodiment of the present invention, the identification module further includes a bracket and a circuit board, wherein the sensor is electrically connected to the circuit board, the bracket is disposed on the circuit board, the optical component is disposed on the bracket, the bracket supports the optical component and holds the optical component in the light-sensing path of the sensor.

[0016] According to one embodiment of the present invention, the light source is disposed on the circuit board and is connected to the sensor through the circuit board.

[0017] According to one embodiment of the present invention, the identification module further includes a transparent cover and a support member, wherein the transparent cover is supported by the support member on the light-sensing path of the sensor.

[0018] According to one embodiment of the present invention, the circuit board further includes a first circuit board and a second circuit board, wherein the sensor is disposed on the first circuit board and the light source is disposed on the second circuit board.

[0019] According to one embodiment of the present invention, the circuit board further includes at least one connecting line, wherein the connecting line electrically connects the first circuit board and the second circuit board.

[0020] According to one embodiment of the present invention, the circuit board further includes a flexible circuit board, wherein the flexible circuit board is disposed on the first circuit board and the second circuit board, and electrically connects the first circuit board and the second circuit board through the flexible circuit board.

[0021] According to one embodiment of the present invention, the sensor is a spectral sensor.

[0022] According to an embodiment of the present invention, the identification module includes a spectral sensor, an imaging sensor, and a beam splitter, wherein the beam splitter is located in the optical path of the spectral sensor and the imaging sensor, and the detection light is split into a first detection light and a second detection light by the beam splitter, wherein the first detection light is deflected by the beam splitter and reaches the spectral sensor, and the second detection light is transmitted through the beam splitter and reaches the imaging sensor, wherein the spectral sensor acquires the spectral information of the object under test through the first detection light, and the imaging sensor acquires the image information of the object under test by detecting the second detection light.

[0023] According to one embodiment of the present invention, the identification module further includes a light homogenizer, wherein the light homogenizer is disposed between the light splitter and the spectral sensor, the light homogenizer homogenizes the light, and the spectral sensor obtains spectral information for liveness detection.

[0024] According to one embodiment of the present invention, the identification module further includes a lens group located between the imaging sensor and the beam splitter, which adjusts the light before it is received by the imaging chip.

[0025] According to one embodiment of the present invention, the imaging sensor and the lens group are arranged in a horizontal direction, wherein the spectral sensor and the light homogenizer are arranged in a height direction.

[0026] According to another aspect of the present invention, the present invention further provides a method for detecting live fingerprints, comprising:

[0027] (a) Obtaining the light intensity information from fingerprint acquisition;

[0028] (b) Obtain fingerprint images and spectral information based on the acquired light intensity information; and

[0029] (c) Compare the fingerprint image and the spectral information with the recorded reference information. If the matching degree is higher than the threshold, the input verification is successful; otherwise, the verification fails.

[0030] According to one embodiment of the present invention, the detection method further includes: correcting light intensity information, wherein the light intensity information correction includes an image processing method that compensates for the surrounding mean.

[0031] According to one embodiment of the present invention, in image information correction, the intensity value of the spectral pixel is replaced with the intensity value of the weighted average of the intensities of nearby ordinary physical pixels, thereby generating corrected image parameters to obtain a fingerprint image.

[0032] According to an embodiment of the present invention, the detection method further includes: a step of correcting the spectral information corresponding to the spectral pixel, wherein the intensity value of the current spectral pixel is divided by or subtracted from the weighted average intensity value of the neighboring ordinary pixels to obtain a relative intensity, which is used as the corrected spectral information for subsequent processing.

[0033] According to an embodiment of the present invention, the detection method further includes a liveness determination step, which involves calculating the correlation coefficient R between the effective corrected spectral information extracted after processing the original data and the recorded reference spectral information; when the correlation coefficient R is greater than the corresponding threshold, the person is determined to be a live person; otherwise, the person is determined to be a non-live person.

[0034] According to an embodiment of the present invention, the detection method further includes the following steps:

[0035] In the case of n valid entries, the correlation coefficient R of the spectral characteristic parameters of each entry with the other n-1 entries is calculated. The lowest correlation coefficient R_min is taken and calculated with the system set parameter k using a specific formula to obtain the judgment threshold R_t for the entry comparison. When there are n-1 or more entries greater than the corresponding judgment threshold, the entry is considered to be a live organism; otherwise, it is considered to be a non-live organism. The specific formula is: R_t = max(R_min, k).

[0036] According to one embodiment of the present invention, the detection method further includes a step of determining the consistency of spectral features:

[0037] Each time data is entered, the spectral information is processed according to the comparison process, and the correlation coefficient between the entered data and the previously entered spectral information is calculated. If the coefficient is less than the system set value m, the data entry fails.

[0038] According to one embodiment of the present invention, the detection method further includes the step of updating the recorded data:

[0039] After each successful detection, the correlation coefficient of the spectral information of this detection is calculated with n entered data, and the corresponding mean value R_atest is obtained. This mean value is then compared with the mean correlation coefficients R_a1 to n among the n entered data. If R_atest is greater than one or more of R_a1 to n, the data from this test is used to replace the smallest data among the entered data of R_a1 to n.

[0040] The further objects and advantages of the invention will become fully apparent from the following description and accompanying drawings.

[0041] These and other objects, features and advantages of the present invention will become fully apparent from the following detailed description and accompanying drawings. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of a live fingerprint detection device according to a first preferred embodiment of the present invention.

[0043] Figure 2 This is a schematic diagram of the frame of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0044] Figure 3 This is a partial structural schematic diagram of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0045] Figure 4 This is a schematic diagram of the overall structure of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0046] Figures 5A to 5C This is a schematic diagram of an optional embodiment of the overall structure of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0047] Figures 6A to 6D This is a schematic diagram of an optional embodiment of the overall structure of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0048] Figure 7 This is a schematic diagram of the structural framework of a sensor of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0049] Figure 8Aand Figure 8B This is a schematic diagram of the microstructure of a sensor of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0050] Figure 9 This is a schematic diagram of the frame structure of the spectral sensor of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0051] Figure 10 This is a cross-sectional view of the spectral sensor of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0052] Figure 11 This is a schematic diagram of the physical pixels of the spectral sensor of the live fingerprint detection device according to the first preferred embodiment of the present invention.

[0053] Figure 12 This is a schematic diagram of the frame of a live fingerprint detection device according to a second preferred embodiment of the present invention.

[0054] Figure 13 This is a flowchart of a live fingerprint detection method according to another preferred embodiment of the present invention.

[0055] Figure 14 This is a schematic diagram of the correlation coefficient of a live fingerprint detection method according to another preferred embodiment of the present invention after linear fitting.

[0056] Figure 15 This is a schematic diagram of the method framework of a live fingerprint detection method according to another preferred embodiment of the present invention. Detailed Implementation

[0057] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0058] Those skilled in the art should understand that, in the disclosure of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the above terms should not be construed as limiting this invention.

[0059] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0060] Overview of the detection principle of the live fingerprint detection device in this invention

[0061] like Figure 1 As shown, due to the presence of physiological features such as capillaries (blood) and sweat pores in human skin, it is relatively more difficult to forge than fingerprint patterns. Furthermore, these physiological features cause the skin to absorb / reflect different wavelengths of the spectrum, indicating that liveness detection can be achieved by using the spectral information reflected from the skin. Specifically, by conducting reflectance spectral tests on real fingers and fingerprint mold materials, it is found that in the wavelength range of 300nm-1100nm, the reflectance spectra of real fingers and fingerprint mold materials differ significantly. Figure 1 Taking tests on materials such as silicone, paper, and human skin as examples, the reflectance spectral data corresponding to real human fingers and fingerprint materials differ significantly. Therefore, it is feasible to determine liveness based on the received reflectance spectra.

[0062] Referring to the accompanying drawings of this invention Figures 1 to 15 As shown, a live fingerprint detection device and method according to the present invention will be described below. The live fingerprint detection device includes a light source 10 and a recognition module 20, wherein the light source 10 emits illumination light to the finger to be tested, and the recognition module 20 detects the fingerprint by detecting the light reflected from the finger. The recognition module 20 includes an optical component 21 and at least one sensor 22, wherein the optical component 21 is located in the optical path of the sensor 22. It is worth noting that, in this preferred embodiment of the present invention, the sensor 22 is an image sensor or a spectral sensor. Preferably, the optical component 21 is a lens assembly, and the optical component 21 further includes at least one lens element. More preferably, the optical component 21 is used to transmit the imaging information of the fingerprint to the sensor 22, wherein the FOV of the optical component 21 is between 80 degrees and 130 degrees, the back focal length is between 0.3 mm and 5 mm, and the total optical length is between 1 mm and 10 mm.

[0063] The light source 10 is used to illuminate the finger to be tested and needs to be selected with a certain spectral width (>30nm). The light source can emit monochromatic light or mixed light as needed.

[0064] It is worth mentioning that, in this preferred embodiment of the present invention, the light source 10 is disposed on the recognition module 20 or disposed adjacent to the recognition module 20. The light emitted by the light source 10 reaches the finger to be tested, and the detection light reflected by the finger to be tested passes through the optical component 21 of the recognition module 20 to the sensor 22, and then the sensor 22 performs a liveness determination, thereby realizing the liveness detection of the fingerprint.

[0065] like Figure 3 As shown, in this preferred embodiment of the present invention, the identification module 20 further includes a bracket 23 and a circuit board 24, wherein the sensor 22 is electrically connected to the circuit board 24. Preferably, the bracket 23 is disposed on the circuit board 24, and the optical component 21 is disposed on the bracket 23, with the bracket 23 supporting the optical component 21 and holding the optical component 21 in the photosensitive path of the sensor 22. It is worth mentioning that the circuit board 24 can be, but is not limited to, a flexible printed circuit board (FPC), a rigid printed circuit board (PCB), a rigid-flex PCB (F-PCB), a ceramic substrate, etc. The circuit board 24 can be used for driving, controlling, processing, and outputting data from the light source and the sensor.

[0066] In at least one embodiment of the present invention, since the live fingerprint detection device needs to be miniaturized, the light source 10 can be integrated into the circuit board 24, such as... Figure 4 As shown, it may be integrated into the bracket 23. That is, the light source 10 is fixed to the circuit board 24, or the light source 10 is fixed to the bracket 23. Preferably, the light emission path of the light source 10 is not parallel to the light-sensing path of the sensor 22.

[0067] like Figure 5A and Figure 5B A specific embodiment of the identification module 20 of the present invention is shown. In this preferred embodiment, the identification module 20 further includes a transparent cover plate 25 and a support member 26, wherein the transparent cover plate 25 is supported by the support member 26 in the light-sensing path of the sensor 22. The transparent cover plate 25 is used to place the object to be tested, such as a finger, and to acquire the reflected light information of the object to be tested through the transparent cover plate 25. As an example, the light emitted by the light source 10 passes through the transparent cover plate 25 and illuminates the fingerprint of the finger, and the reflected light is reflected by the transparent cover plate 25 into the sensor 22.

[0068] It is worth mentioning that, in this preferred embodiment of the present invention, the identification module 20 has a dual-support structure, wherein the support member 26 supports the transparent cover plate 25, and the support member 26 and the transparent cover plate 25 enclose the bracket 23 and the optical component 21 fixed by the bracket 23 inside. The support member 26 is an outer support structure, and the bracket 23 is an inner support structure fixed inside the support member 26.

[0069] Preferably, the support member 26 is disposed on the circuit board 24, wherein the upper end of the support member 26 is fixed to the transparent cover plate 25, and the other end of the support member 26 is fixed to the circuit board 24, and the support member 26, the circuit board 24, and the transparent cover plate 25 form a closed space to prevent dust from entering. Preferably, in this preferred embodiment of the present invention, the transparent cover plate 25 is supported by the support member 26, and the distance between the transparent cover plate 25 and the circuit board 24 is less than 7 mm. In some embodiments of the present invention, the support member 26 and the bracket 23 are integrated, that is, they are integrated into a single structural component for fixing and supporting the optical component 21 and the transparent cover plate 25.

[0070] The light source 10 is disposed on the circuit board 24 and electrically connected to the circuit board 24. Preferably, in this preferred embodiment of the invention, the light source 10 is located inside the support member 26, wherein the light source 10 is disposed adjacent to the outside of the bracket 23, and the light emission path of the light source 10 is not parallel to the light-sensing path of the sensor. Optionally, in other alternative embodiments of the invention, the light source 10 is disposed on the bracket 23, and the light source 10 is electrically connected to the circuit board 24.

[0071] like Figure 5B As shown, according to another aspect of the present invention, the present invention further provides another optional embodiment of the identification module, wherein the circuit board 24 further includes a first circuit board 241 and a second circuit board 242, wherein the sensor 241 is disposed on the first circuit board 241, and the light source 10 is disposed on the second circuit board 242. The bracket 23 is fixed to the first circuit board 241, and the optical component 21 is fixed to the bracket 23 and located in the photosensitive path of the sensor 22.

[0072] The circuit board 24 further includes a flexible board 244, wherein the flexible board 244 is disposed on the first circuit board 241 and the second circuit board 242, and the first circuit board 241 and the second circuit board 242 are electrically connected through the flexible board 244 to realize the conduction of the circuit board 24.

[0073] likeFigure 5C As shown, in an optional embodiment of the present invention, the identification module 20 further includes at least one light homogenizer 28, wherein the light homogenizer 28 is disposed in the photosensitive path of the light source 10 to homogenize the light emitted by the light source. The light homogenizer 28 is disposed at the emitting end of the light source 10, wherein the light emitted by the light source 10 is irradiated onto the transparent cover plate 25 through the light homogenizer 28.

[0074] The accompanying drawings of this invention Figures 6A to 6C As shown, an identification module 20 according to another preferred embodiment of the present invention is illustrated. The difference from the above preferred embodiment is that, in this preferred embodiment of the present invention, the transparent cover plate 25 of the identification module is disposed on the bracket 23. The bracket 23 includes a bracket body 231, a lens support portion 232, and a cover plate support portion 233, wherein the lens support portion 232 is located at the upper end of the cover plate support portion 233. That is, the bracket 23 supports the transparent cover plate 25 above the optical component 21. The lens support portion 232 extends inward from the lens body 231 and forms a support structure with a light-transmitting hole inside the bracket 23, wherein the cover plate support portion 233 extends integrally upward from the bracket body 231 to fix and support the transparent cover plate 25. In short, in this preferred embodiment of the present invention, the bracket 23 is a double-layer bracket structure, wherein the transparent cover plate 25 is supported above the optical component 21 by the cover plate support portion 233 of the bracket 23, and the optical component 21 is supported below the transparent cover plate 25 by the lens support portion 232 of the bracket 23.

[0075] The lens support portion 232 of the bracket 23 divides the internal space of the bracket 23 into an upper accommodating space 234 and a lower accommodating space 235, wherein the optical component 21 is held in the upper accommodating space 234 of the bracket 23, and the sensor 22 is held in the lower accommodating space 235 of the bracket 23. The light source 10 and the sensor 22 are disposed on the circuit board 24 and electrically connected to the circuit board 24. The circuit board 24 further includes a first circuit board 241 and a second circuit board 242, wherein the sensor 241 is disposed on the first circuit board 241, and the light source 10 is disposed on the second circuit board 242. The bracket 23 is fixed to the first circuit board 241, and the optical component 21 is fixed to the bracket 23 and located in the photosensitive path of the sensor 22.

[0076] like Figure 6AUnlike the preferred embodiment described above, the light source 10 is disposed on the lens support portion 232 of the bracket 23, wherein the light source 10 is located above the sensor 22. Preferably, the light source 10 is disposed on the bracket lens portion 232 of the bracket 23, and the light emission path of the light source is not parallel to the light-sensing path of the sensor.

[0077] Preferably, in this preferred embodiment of the present invention, the first circuit board 241 and the second circuit board 242 are electrically connected; the transparent cover plate 25 is disposed on the upper end of the bracket 23 and held on the photosensitive path of the sensor 22. The sensor 22, the bracket 23, and the optical component 22 supported by the bracket 23 form a substantially sealed space; the transparent cover plate 25, the bracket 23, and the optical component 21 form a substantially sealed space.

[0078] like Figure 6B As shown, the recognition module 20 of the live fingerprint detection device of the present invention is further provided with at least one heat dissipation hole 201, wherein the at least one heat dissipation hole 201 connects the internal space of the recognition module to the external environment, thereby reducing the internal temperature of the recognition module 20. As an example, the bracket 23 is provided with an opening, or when the transparent cover plate 25 is fixed, the transparent cover plate 25 and the support member 26 are connected at the connection position of the transparent cover plate 25 and the support member 26 by means of three-sided adhesive, so that the upper part of the space is not completely closed and there is a gap for ventilation and heat dissipation.

[0079] like Figure 6C As shown, according to another aspect of the present invention, the first circuit board 241 and the second circuit board 242 are electrically connected. The first circuit board 241 and the second circuit board 242 are connected via pins. Specifically, the circuit board 24 further includes at least one connecting line 243, wherein the connecting line 243 electrically connects the first circuit board 241 and the second circuit board 242. It is understood that the connecting line may be, but is not limited to, metal pins, wherein one end of the connecting line 243 is connected to the second circuit board 242, and the other end of the connecting line 243 is connected to the first circuit board 241, so as to realize the connection between the first circuit board and the second circuit board.

[0080] Since the bracket 23, the optical component 21, and the first circuit board 241 form a closed space, it is difficult to fix the connecting wire 243 to the first circuit board 241 in a conductive manner from a manufacturing perspective. Preferably, the bracket 23 is provided with a corresponding connecting hole 230, wherein the connecting hole 230 of the bracket 23 is directly opposite the connection position of the first circuit board 241, so that when the second circuit board 242 is placed on the bracket 23, the connecting wire 243 can be connected to the first circuit board 241 through the connecting hole 230 of the bracket 23, and then the connecting wire 243 is fixed to the first circuit board 241 by processes such as welding and gluing. Furthermore, the connecting wire 243 is only connected to the first circuit board 241 after the bracket 23 is fixed to the first circuit board 241. Therefore, preferably, the first circuit board 241 has a connection through hole, through which the connecting wire 243 passes at least partially, so that the connecting wire 243 can be fixed and connected to the first circuit board 241 from the back side of the first circuit board 241.

[0081] like Figure 6D As shown, in an optional embodiment of the present invention, the identification module 20 further includes at least one light homogenizer 28, wherein the light homogenizer 28 is located in the photosensitive path of the light source 10 and is used to homogenize the light emitted by the light source. The light homogenizer 28 is disposed at the emitting end of the light source 10, wherein the light emitted by the light source 10 is irradiated onto the transparent cover plate 25 through the light homogenizer 28.

[0082] Preferably, for a scheme in which the light source is placed on the first circuit board, the live fingerprint detection device further includes a heat sink, which is placed below the light source to quickly dissipate the heat generated by the light source.

[0083] Figure 6 to Figure 11As shown, the sensor 22 is a spectral sensor, which includes a filter structure and an image sensor. The filter structure is located on the photosensitive path of the image sensor and is a broadband filter structure in the frequency domain or wavelength domain. The pass spectra of different wavelengths of the filter structure are not completely the same at different locations. The filter structure can be a metasurface, photonic crystal, nanopillar, multilayer film, dye, quantum dot, MEMS (microelectromechanical systems), FP etalon, cavity layer, waveguide layer, diffraction element, or other structures or materials with filtering properties. For example, in this embodiment, the filter structure can be the light modulation layer in Chinese patent CN201921223201.2. The image sensor can be a CMOS image sensor (CIS), CCD, array photodetector, etc. In addition, the spectral device also includes a data processing unit, which can be a processing unit such as MCU, CPU, GPU, FPGA, NPU, ASIC, etc., which can export the data generated by the image sensor to an external source for processing.

[0084] The spectral sensor is used to acquire fingerprint path image information and finger spectral feature information to verify finger biometrics. The chip size ranges from 1 / 9' to 1 / 1.6', with an imaging spatial resolution of over 50,000 pixels, and possesses the ability to distinguish the spectrum of the light being measured with a spectral resolution equivalent to below 30nm. The spectral sensor can be mounted on the circuit board using COB, CSP, or FC packaging processes.

[0085] Specifically, the working principle of the spectral sensor is as follows: the intensity signal of the incident light at different wavelengths λ is denoted as f(λ), and the transmission spectrum curve of the filter structure is denoted as T(λ). The spectral sensor has m sets of filter structures, each with a different transmission spectrum, also called a "structural unit," which can be denoted as Ti(λ) (i = 1, 2, 3, ..., m). Each set of filter structures has a corresponding physical pixel below it, which detects the light intensity information Ii modulated by the filter structure. In a specific embodiment of this application, one physical pixel corresponding to one set of structural units is used as an example for explanation, but it is not limited to this. In other embodiments, multiple physical pixels can also form a set corresponding to one set of structural units.

[0086] The relationship between the spectral distribution of incident light and the measurements from the image sensor can be expressed by the following formula:

[0087] Ii=Σ(f(λ)·Ti(λ)·R(λ))

[0088] Where R(λ) is the response of the image sensor, denoted as:

[0089] Si(λ)=Ti(λ)·R(λ)

[0090] The above equation can then be extended into matrix form:

[0091]

[0092] Where Ii (i = 1, 2, 3, ..., m) are the responses of the image sensors after the light to be measured passes through the broadband filter structure, corresponding to the light intensity information of m image sensors, also known as m "physical pixels," which is a vector of length m. S is the system's response to light of different wavelengths, determined by the transmittance of the filter structure and the quantum efficiency of the image sensor response. S is a matrix, where each row vector corresponds to the response of a structural unit to incident light of different wavelengths. Here, the incident light is sampled discretely and uniformly, with a total of n sampling points. The number of columns in S is the same as the number of sampling points of the incident light. Here, f(λ) is the light intensity of the incident light at different wavelengths λ, which is the incident light spectrum to be measured.

[0093] In practical applications, the system's response parameter S is known. By using the light intensity reading I from the image sensor, the spectrum f of the input light can be obtained through algorithmic deduction (which can be understood as spectral recovery). The process can employ different data processing methods depending on the specific circumstances, including but not limited to: least squares, pseudo-inverse, equalization, least-norm, artificial neural networks, etc.

[0094] The above example, using one physical pixel corresponding to a set of structural units, illustrates how to recover spectral information, also known as a "spectral pixel," using m sets of physical pixels (i.e., pixels on an image sensor) and their corresponding m sets of structural units (identical structures on the modulation layer are defined as structural units). It is worth noting that in this embodiment, multiple physical pixels can also correspond to a set of structural units. Further, a set of structural units and at least one corresponding physical pixel constitute a unit pixel; in principle, at least one unit pixel constitutes a spectral pixel.

[0095] Based on the above implementation method, arraying the spectral pixels can realize a snapshot-type spectral imaging device.

[0096] For example, such as Figure 8A and Figure 8B As shown, an image sensor with 1896*1200 pixels is used. Figure 8A(A portion of the image sensor area is shown.) Simultaneously, m=4 is selected, meaning a 4x4 pixel unit is chosen to form a spectral pixel. This results in 474x300 independent spectral pixels, each of which can have its spectral result calculated individually using the method described above. By combining this image sensor with lens groups and other components, snapshot-style spectral imaging of the object under test can be performed, enabling the acquisition of spectral information for every point on the object in a single exposure.

[0097] Based on this, the selection method of spectral pixels can be rearranged according to actual needs, without making any adjustments to the image sensor, to improve spatial resolution. For example... Figure 8B As shown, you can select a close arrangement of solid and dashed boxes to increase the spatial resolution in the example above from 474*300 to nearly 1896*1200.

[0098] Furthermore, for the same image sensor, the spatial resolution and spectral resolution can be rearranged as needed. For example, in the above example, when higher spectral resolution is required, 8*8 unit pixels can be used to form a spectral pixel; when higher spatial resolution is required, 3*3 physical pixels can be used to form a spectral pixel.

[0099] In other words, the spectral sensor acquires light intensity information, which can be used for both imaging and spectral reconstruction. For example, in a live fingerprint detection device, the light intensity information can include image information and spectral information; the image information is used for fingerprint pattern image reconstruction, and the spectral information is used to determine liveness.

[0100] In one embodiment of the present invention, the spectral sensor preferably has a modulation region and an unmodulated region. The modulation region refers to the optical path of the image sensor having a filter structure, while the unmodulated region corresponds to the absence of a filter structure. That is, the incident light in the modulation region is modulated by the filter structure before being received by the image sensor. The unmodulated region is not modulated; for example, when the image sensor is a CMOS chip, the unmodulated region is directly implemented as black and white pixels (i.e., no Bayer array is provided on the CMOS chip). Preferably, the modulation region is mainly used to acquire spectral information, and the unmodulated region acquires image information. In some embodiments, the unmodulated region can also be implemented as a Bayer array, a microlens array, a convex lens, a concave lens, a Fresnel lens, etc., to adjust the incident light.

[0101] Preferably, in this preferred embodiment of the present invention, the area of ​​the modulation region accounts for 10%-50% of the effective area of ​​the spectral chip, preferably 12%-25%, and optionally at least a portion of the modulation region and the non-modulation region are spaced apart; therefore, during the processing and analysis, the image information of the non-modulation region surrounding the modulation region can be combined with the spectral information of the modulation region, and the image information can be used to optimize the spectral information, for example, to remove background noise, making the spectral information more accurate; specifically, the average value of the image information of the surrounding non-modulation region can be taken, and then the value of the modulation region can be divided by or subtracted from the average value of the image information of the surrounding non-modulation region; spectral information can also be used to assist image information in image restoration. Generally speaking, spectral information has more information, and since the modulation region has structural units, its information is different from that of the non-modulation region. Therefore, there will be information gaps in this region during imaging. Therefore, the spectral information obtained from the modulation region can be used to calculate and compensate for the image information in this region, or correct the image information of its adjacent regions. For example, Figure 11 As shown, taking the filter structure corresponding to one physical pixel as an example, there are two physical pixels between two adjacent filter structures; that is, one physical pixel with a structural unit is surrounded by eight physical pixels.

[0102] In at least one embodiment of the present invention, since the modulation region may lack image information for calculation, it can also use the image information values ​​obtained from the physical pixels of the surrounding non-modulation regions to calculate the image information value of the modulation region. Specifically, the average value of the image information of the surrounding physical pixels can be used as the image information value of the modulation region, thereby making the whole image more complete. Taking the physical pixels corresponding to one structural unit surrounded by 8 physical pixels in the figure below as an example, the image information value of the middle modulation region can be calculated using the surrounding 8 physical pixels; or the average value of the surrounding 24 physical pixels can be used to calculate the image information value corresponding to the middle modulation region.

[0103] It is worth mentioning that, in this embodiment, the spectral information does not necessarily need to be reconstructed to perform liveness detection; instead, liveness detection can be performed directly based on the response. Specifically, the following steps are taken: acquiring reference spectral response data of the image sensor of the spectral analysis device to a reference object; acquiring recognition spectral response data of the image sensor of the spectral analysis device to the object to be identified; and determining the identification result of the object to be identified based on the comparison result between the reference spectral response data and the recognition spectral response data.

[0104] Referring to the accompanying drawings of this invention Figure 12As shown, a live fingerprint detection device according to another aspect of the present invention will be described below. The identification module 20 of the live fingerprint detection device includes a spectral sensor 221A, an imaging sensor 222A, and a beam splitter 27A, wherein the beam splitter 27A is located in the optical path of the spectral sensor 221A and the imaging sensor 222A, i.e., the incident light reaches the beam splitter 27A. The detection light is split into a first detection light and a second detection light by the beam splitter 27A, wherein the first detection light is deflected by the beam splitter 27A and reaches the spectral sensor 221A, and the second detection light is transmitted through the beam splitter 27A and reaches the imaging sensor 222A. The spectral sensor 221A acquires the spectral information of the object under test through the first detection light, and the imaging sensor 222A acquires the image information of the object under test by detecting the second detection light.

[0105] Preferably, the recognition module 20 further includes a light homogenizer 28A, wherein the light homogenizer 28A is disposed between the light splitter 27A and the spectral sensor 221A. The light homogenizer 28A homogenizes the light, and the spectral sensor 221A obtains spectral information for liveness detection. It should be noted that since the surface to be tested is often uneven, such as fingerprints with valleys and ridges, changes in the corresponding area during testing will cause different spectral responses in different areas, increasing the difficulty of liveness detection. Therefore, by using the light homogenizer 28A to homogenize the light, even if the area changes during testing, the overall spectral information remains unchanged.

[0106] For example, during fingerprint liveness detection, the tester's finger placement causes a certain angle of deflection, which alters the fingerprint valleys and ridges corresponding to the spectral sensor. This results in changes to the spectral information reaching the sensor, requiring additional processing for accurate judgment. However, after uniform illumination, since the light source remains stationary, the valleys and ridges remain unchanged overall. Therefore, deflection does not cause significant changes in the spectral information, allowing for simpler and more efficient liveness detection. Preferably, the recognition module 20 of the live fingerprint detection device further includes a lens group 29A, located between the imaging sensor 222A and the beam splitter 27A. The lens group 29A adjusts the light before it is received by the imaging chip, improving image quality, for example, resulting in clearer images.

[0107] Given the size requirements in practical applications, such as in mobile phones and wearable devices, where dimensions in a certain direction need to be limited, taking the height direction as an example, the imaging sensor 222A paired with the lens group 29A generally has focal length requirements, and the size of the lens group is usually relatively large. Preferably, the imaging sensor 222A and the lens group 29A are arranged in the horizontal direction, while the spectral sensor 221A paired with the light homogenizer 28A is arranged in the height direction (vertical direction). That is, after the incident light enters the beam splitter 27A along the height direction, the transmitted portion enters the light homogenizer 28A, is homogenized, and then reaches the spectral sensor 221A; while the deflected portion enters the lens group 29A along the horizontal direction, is adjusted, and then received by the imaging sensor 222A.

[0108] It is worth mentioning that, since the recognition module of this preferred embodiment of the present invention uses a spectral sensor, it can acquire spectral information and use the spectral information to determine whether the object to be detected is a living person, thereby making the security performance of fingerprint recognition higher.

[0109] See attached document Figure 13 As shown, the present invention further provides a live fingerprint detection method based on the above-mentioned live fingerprint detection device, wherein the spectral sensor 221A obtains raw data, namely light intensity information, the light intensity information including image information and spectral information, and the raw data is corrected for image information and spectral information respectively; then, the fingerprint recognition algorithm and the liveness algorithm are respectively used to compare the fingerprint image and spectral information with the corresponding reference information extracted during the recording to obtain the matching degree; when the matching degree of both is higher than the threshold, the input verification is passed; otherwise, the output verification fails.

[0110] Image information correction and spectral information correction include image processing methods such as binning. Therefore, in this preferred embodiment of the invention, the live fingerprint detection method further includes steps of image information correction and spectral information correction. In image information correction, the intensity value of the spectral pixel (which can be understood as a filter structure corresponding to a physical pixel) is replaced with the intensity value of a weighted average of the intensities of nearby ordinary physical pixels, thereby generating corrected image information (image data). The average value can be selected from several neighboring (e.g., 4, 8, 24, 80) ordinary physical pixels. When the number is greater than 4, the weighting kernel used for the weighted average can be a uniform kernel (equal weighting for all physical pixels) or a Gaussian kernel. For example... Figure 11In the embodiment shown, a 5*5 Gaussian kernel can be used, as shown in Table 1. The middle 0 represents a spectral pixel. That is, the light intensity information (image information) at this location needs to be obtained by Gaussian kernel weighted average of the light intensity information (image information) of the surrounding 24 physical pixels. That is, the light intensity information value of the relevant material pixel is multiplied by the sum of the corresponding coefficients and then divided by the sum of the weights.

[0111] Table 1

[0112]

[0113]

[0114] For acquiring spectral information, it is necessary to avoid the influence of brightness at different locations on spectral verification. For example, the reflectivity of fingerprint valleys and ridges differs, resulting in different levels of brightness, which may affect the judgment of the spectral signal of the object under test. Therefore, the live fingerprint detection method of this preferred embodiment further includes a step of correcting the intensity of spectral pixels. For example, the intensity value of the current spectral pixel can be divided by or subtracted from the weighted average (binning) value of neighboring ordinary pixels to obtain the relative intensity, which is used as corrected spectral information for subsequent processing. Furthermore, the corrected spectral information can be filtered according to specific rules to remove excessively large or small values, thereby improving the effectiveness of the corrected spectral information. Figure 11 For example, you can take the average intensity value of the 8 physical pixels surrounding the spectral pixel, and then divide or subtract the average intensity value of the 8 physical pixels from the intensity value of the spectral pixel to obtain the corrected spectral information.

[0115] See attached document Figure 14 As shown, the live fingerprint detection method of the present invention further includes a liveness detection algorithm step. Effective corrected spectral parameters (which can also be understood as corrected spectral information) are extracted from the raw data (light intensity information) after processing, and the correlation coefficient R between the parameters and the reference spectral information is calculated (for example, the Pearson correlation coefficient can be used). When the correlation coefficient R is greater than the corresponding threshold, the person is determined to be alive; otherwise, they are determined to be inactive. Since the correlation coefficient R needs to be calculated in this invention, both the entered information and the detection information are vectorized into a one-dimensional vector.

[0116] Furthermore, the live fingerprint detection method of the present invention further includes the steps of threshold selection and application. For different data entries, due to potential changes in various conditions during entry, the noise power ratio (signal-to-noise ratio) varies with each data acquisition. Entry with a high signal-to-noise ratio generally results in a higher correlation coefficient between the corresponding spectral information and other entered reference spectral information; conversely, entry with a low signal-to-noise ratio generally results in a lower correlation coefficient. Therefore, using a uniform threshold for judgment can easily introduce misjudgments. To address this, this application eliminates the dynamic selection of a threshold and its corresponding application method, enabling more accurate liveness verification.

[0117] Given n valid data entries (e.g., a set of valid entries consists of 10 entries), calculate the correlation coefficient R between the current spectral information and the spectral information from the other n-1 entries. Take the lowest correlation coefficient R_min and use it in a specific formula with the system-defined parameter k to calculate the judgment threshold R_t for this entry. In actual use, calculate the correlation coefficient between the data to be tested and the n previously entered data, and compare it with the corresponding judgment threshold R_t (1~10). If n-1 or more correlation coefficients are greater than the corresponding judgment threshold, the test is considered to be a live organism; otherwise, it is considered to be a non-live organism.

[0118] Furthermore, the live fingerprint detection method of the present invention further includes a fingerprint enrollment step. During enrollment, the consistency of the enrolled spectral features is assessed. Due to interference from random factors such as ambient light or the state of the finger to be enrolled, the enrolled spectral information may become unstable, thus affecting the user experience and accuracy. Therefore, a consistency assessment of the spectral information is necessary during enrollment.

[0119] For example, the data entry requires a series of N (2 to 20) consecutive entries using the same finger. During each entry, the spectral parameters are processed according to the comparison procedure, and the correlation coefficient between the entered data and the corresponding spectral parameter data that has already been entered is calculated. If the coefficient is less than the system setting value m, the entry fails.

[0120] Optionally, the correlation coefficient of each entered data can be compared with the entered data in the database corresponding to the specific prosthetic material already existing in the system. If the correlation coefficient of multiple (n=1 to 3) data points is greater than the system threshold q, then the data entry fails. If multiple consecutive (n=2 to 5) data entry failures occur, then the entire set of data entry fails and a new set of data entry needs to be performed.

[0121] Furthermore, the live fingerprint detection method of the present invention further includes a step of updating the entered data. Considering that the system or the object to be tested may change over a long period of time, it is necessary to update the entered data. For example, after each successful detection, the average correlation coefficient R_atest of the comparison between the spectral parameters of this detection and 10 entered data stored in the system is calculated. This average correlation coefficient R_atest is then compared with the average correlation coefficients R_a1 to 10 among the 10 entered data. If R_atest is greater than one or more of R_a1 to 10, the smallest one among R_a1 to 10 is selected, and the corresponding entered data is replaced with the data from this test. It should be noted that the 10 entered data are only used as an example and do not constitute a limitation. The number of data is not necessarily 10; it can be greater than 10 or less than 10, and can be adjusted according to requirements.

[0122] Referring to the accompanying drawings of this invention Figure 15 As shown, the live fingerprint detection method according to the preferred embodiment of the present invention will be explained in the following description. The live fingerprint detection method includes the following steps:

[0123] (a) Obtaining the light intensity information from fingerprint acquisition;

[0124] (b) Obtain fingerprint images and spectral information based on the acquired light intensity information; and

[0125] (c) Compare the fingerprint image and the spectral information with the recorded reference information. If the matching degree is higher than the threshold, the input verification is successful; otherwise, the verification fails.

[0126] In the aforementioned live fingerprint detection method, the detection method further includes: light intensity information correction, wherein the light intensity information correction includes an image processing method of surrounding mean compensation (binning). In the image information correction, the intensity of spectral pixels is replaced with the intensity value of a weighted average of the intensities of nearby ordinary physical pixels, thereby generating corrected image parameters.

[0127] In the above-mentioned live fingerprint detection method, the detection method further includes: correcting the spectral information corresponding to the spectral pixel, that is, dividing or subtracting the intensity value of the current spectral pixel by the weighted average (binning) intensity value of the neighboring ordinary pixels to obtain the relative intensity, which is used as the corrected spectral information for subsequent processing.

[0128] In the above-mentioned live fingerprint detection method, the detection method further includes: a liveness determination step, which calculates the correlation coefficient R between the effective corrected spectral information extracted after processing the original data and the recorded reference spectral information. When the correlation coefficient R is greater than the corresponding threshold, the person is determined to be live; otherwise, the person is determined to be non-live.

[0129] In the above-described live fingerprint detection method, the detection method further includes the following steps:

[0130] In the case of n valid entries, the correlation coefficient R of each spectral information is calculated with the other n-1 entries. The lowest correlation coefficient R_min is taken and calculated with the system set parameter k using a specific formula to obtain the judgment threshold R_t for this entry comparison. When there are n-1 or more entries greater than the corresponding judgment threshold, the test is considered to be a live organism; otherwise, it is considered to be a non-live organism. The specific formula is: R_t = max(R_min, k).

[0131] In the above-described live fingerprint detection method, the detection method further includes a step of determining the consistency of spectral features:

[0132] During each data entry, the entered spectral information is processed according to the comparison procedure, and the correlation coefficient between the entered data and the previously entered spectral information is calculated. If the correlation coefficient is less than the system set value m, the data entry fails. Optionally, the spectral parameters at each entry are compared with the spectral parameters of the specific prosthetic material already existing in the system. If any data has a correlation coefficient greater than the system threshold q, the data entry fails.

[0133] In the above-described live fingerprint detection method, the detection method further includes a step of updating the recorded data:

[0134] After each successful detection, the correlation coefficient of the spectral information of this detection is calculated with n entered data, and the corresponding mean value R_atest is obtained. This mean value is then compared with the mean correlation coefficients R_a1 to n among the n entered data. If R_atest is greater than one or more of R_a1 to n, the data from this test is used to replace the smallest data among the entered data of R_a1 to n.

[0135] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1. A live fingerprint detection device, characterized in that, include: A light source, wherein the light generated by the light source is emitted onto the fingerprint to be tested; and An identification module includes at least one sensor and an optical component, the optical component being located in the optical path of the at least one sensor, and the reflected light from the fingerprint to be tested reaching the at least one sensor via the optical component; wherein the identification module further includes a bracket and a circuit board, wherein the sensor is electrically connected to the circuit board, the bracket is disposed on the circuit board, the optical component is disposed on the bracket, the bracket supports the optical component and holds the optical component in the photosensitive path of the sensor, the at least one sensor is configured to acquire fingerprint image information and spectral information based on the reflected light, and the at least one sensor performs live fingerprint determination based on the spectral information.

2. The live fingerprint detection device according to claim 1, wherein the light source is disposed on the circuit board.

3. The live fingerprint detection device according to claim 2, wherein the recognition module further includes a transparent cover and a support member, wherein the support member is sleeved on the outside of the bracket and supports the transparent cover above the optical component.

4. The live fingerprint detection device according to claim 2, wherein the recognition module further includes a transparent cover plate, wherein the transparent cover plate is disposed on the bracket and supported by the bracket above the optical component.

5. The live fingerprint detection device according to claim 1, wherein the bracket includes a bracket body, a lens support and a cover plate support, wherein the lens support is located at the upper end of the cover plate support, and the light source and the second circuit board are disposed on the lens support of the bracket.

6. The live fingerprint detection device according to claim 3 or 4, wherein the circuit board further comprises a first circuit board and a second circuit board, wherein the sensor is disposed on the first circuit board and the light source is disposed on the second circuit board.

7. The live fingerprint detection device according to claim 6, wherein the circuit board further includes at least one connecting line, wherein the connecting line is electrically connected to the first circuit board and the second circuit board.

8. The live fingerprint detection device according to claim 6, wherein the circuit board further includes a flexible circuit board, wherein the flexible circuit board is disposed on the first circuit board and the second circuit board, and is electrically connected to the first circuit board and the second circuit board through the flexible circuit board.

9. The live fingerprint detection device according to claim 1, wherein the recognition module further includes at least one light-shielding element, wherein the at least one light-shielding element is disposed in the light-emitting path of the light source.

10. The live fingerprint detection device according to any one of claims 1 to 9, wherein the sensor is a spectral sensor.

11. The live fingerprint detection device according to claim 1, wherein the recognition module includes a spectral sensor, an imaging sensor, and a beam splitter, wherein the beam splitter is located in the optical path of the spectral sensor and the imaging sensor, the detection light is split into a first detection light and a second detection light by the beam splitter, wherein the first detection light is deflected by the beam splitter and reaches the spectral sensor, and the second detection light is transmitted through the beam splitter and reaches the imaging sensor, the spectral sensor obtains the spectral information of the object to be tested through the first detection light, and the imaging sensor obtains the image information of the object to be tested by detecting the second detection light.

12. A method for detecting live fingerprints, applied to the live fingerprint detection device as described in claim 1, characterized in that, include: (a) Obtaining light intensity information reflected by the fingerprint to be tested through the recognition module; (b) Obtain fingerprint images and spectral information based on the acquired light intensity information; as well as (c) Compare the fingerprint image and the spectral information with the recorded reference information. If the matching degree is higher than the threshold, the input verification is successful; otherwise, the verification fails.

13. The live fingerprint detection method according to claim 12, wherein the detection method further comprises: Light intensity information correction, which includes image processing methods such as surrounding mean compensation.

14. The live fingerprint detection method according to claim 13, wherein in the image information correction, the intensity value of the spectral pixel is replaced with the intensity value of the weighted average of the intensities of nearby ordinary physical pixels, thereby generating corrected image parameters to obtain a fingerprint image.

15. The live fingerprint detection method according to claim 13, wherein the detection method further comprises: The step of correcting the spectral information corresponding to the spectral pixel is to divide or subtract the intensity value of the current spectral pixel from the weighted average intensity value of the neighboring ordinary pixels to obtain the relative intensity, which is used as the corrected spectral information for subsequent processing.

16. The live fingerprint detection method according to claim 15, wherein the detection method further comprises: The steps for determining liveness involve calculating the correlation coefficient R between the effective corrected spectral information extracted from the processed original data and the entered baseline spectral information. If the correlation coefficient R is greater than the corresponding threshold, the person is determined to be alive; otherwise, they are determined to be not alive.

17. The live fingerprint detection method according to claim 15, wherein the detection method further comprises the following steps: In the case of n valid entries, the correlation coefficient R of each spectral information is calculated with the other n-1 entries. The lowest correlation coefficient R_min is taken and calculated with the system set parameter k using a specific formula to obtain the judgment threshold R_t for the entry comparison. When there are n-1 or more entries greater than the corresponding judgment threshold, the test is considered to be a live organism; otherwise, it is considered to be a non-live organism. The specific formula is: R_t = max(R_min, k).

18. The live fingerprint detection method according to claim 12, wherein the detection method further includes a step of determining the consistency of spectral features: Each time data is entered, the spectral information is processed according to the comparison process, and the correlation coefficient between the entered data and the previously entered spectral information is calculated. If the coefficient is less than the system set value m, the data entry fails.

19. The live fingerprint detection method according to claim 12, wherein the detection method further includes the step of updating the recorded data: After each successful detection, the correlation coefficient of the spectral information of this detection is calculated with n entered data, and the corresponding mean value R_atest is obtained. This mean value is then compared with the mean correlation coefficient R_a1~n among the n entered data. If R_atest is greater than one or more of R_a1~n, the data from this test is used to replace the smallest data among the entered data of R_a1~n.

Images