CN117351525B - Image recognition system - Google Patents
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- CN117351525B CN117351525B CN202311656988.2A CN202311656988A CN117351525B CN 117351525 B CN117351525 B CN 117351525B CN 202311656988 A CN202311656988 A CN 202311656988A CN 117351525 B CN117351525 B CN 117351525B
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- 230000001678 irradiating effect Effects 0.000 claims abstract description 3
- 230000036548 skin texture Effects 0.000 claims description 9
- 210000003462 vein Anatomy 0.000 description 18
- 238000003384 imaging method Methods 0.000 description 8
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/13—Sensors therefor
- G06V40/1318—Sensors therefor using electro-optical elements or layers, e.g. electroluminescent sensing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1341—Sensing with light passing through the finger
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- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The embodiment of the application provides an image recognition system, which comprises: a lens, a spectral imager, and a processor; the method comprises the steps of irradiating a target object with light of a first specified wavelength emitted by a visible light source and light of a second specified wavelength emitted by an infrared light source, and collecting reflected light of the target object on the visible light source and the infrared light source by a lens. The micro-nano structure covered on the photoelectric conversion layer of the spectrum imager modulates the reflected light. The photoelectric conversion layer converts an optical signal and electrical data based on the modulated light, and outputs target spectral image data of a target object. The processor analyzes the first spectrum image data of the first specified wavelength and the second spectrum image data of the second specified wavelength from the target spectrum image data based on the response coefficients of the micro-nano structure to the first specified wavelength and the second specified wavelength, and performs target recognition based on the first spectrum image data and the second spectrum image data, so that the complexity of the image recognition system is reduced, and the time consumption of the image recognition system for collecting images is reduced.
Description
Technical Field
The present disclosure relates to the field of image recognition technologies, and in particular, to an image recognition system.
Background
In various application scenes such as entrance guard management, identity authentication needs to be performed on users, and control operations such as entrance guard opening can be performed. In order to ensure the accuracy of user identity authentication, palm print recognition and palm vein recognition are combined in more and more scenes.
In the prior art, when palm print and palm vein recognition is performed, a monocular system can acquire a visible light image and a near infrared light image in a time-sharing image acquisition mode, then palm print recognition is performed on the visible light image, and palm vein recognition is performed on the near infrared light image.
However, since the monocular system includes only one lens, when the visible light image and the near infrared light image are collected in a time-sharing manner, the visible light image is generally collected by using the visible light filter, and then the near infrared light image is collected by switching to the near infrared light filter. Therefore, the optical filter needs to be switched in the image acquisition process, so that the monocular system is complex in structure, and the time for acquiring the image is long due to the fact that the optical filter needs a certain time for switching.
Disclosure of Invention
An object of an embodiment of the present application is to provide an image recognition system, so as to reduce complexity of the image recognition system and reduce time consumption of the image recognition system to acquire images. The specific technical scheme is as follows:
The embodiment of the application provides an image recognition system, which comprises: a lens, a spectral imager, and a processor;
the method comprises the steps that light with a first specified wavelength emitted by a visible light source and light with a second specified wavelength emitted by an infrared light source are used for irradiating a target object, the lens is used for collecting reflected light of the target object on the visible light source and the infrared light source, wherein the visible light source is used for presenting surface grains of the target object, and the infrared light source is used for presenting living body marks of the target object;
the photoelectric conversion layer of the spectrum imager is covered with a micro-nano structure, and the micro-nano structure is used for modulating the reflected light;
the photoelectric conversion layer of the spectrum imager is used for converting optical signals and electric data based on the light modulated by the micro-nano structure and outputting target spectrum image data of the target object;
the processor is used for analyzing first spectrum image data of the first specified wavelength and second spectrum image data of the second specified wavelength from the target spectrum image data based on response coefficients of the micro-nano structure to the first specified wavelength and the second specified wavelength, and performing target identification based on the first spectrum image data and the second spectrum image data; wherein the response coefficient is used for distinguishing spectral image data of different wavelengths from the spectral image data obtained by the photoelectric conversion layer.
Optionally, each photoelectric conversion unit in the photoelectric conversion layer is covered with a micro-nano structure; or,
and part of the photoelectric conversion units in the photoelectric conversion layer are covered with micro-nano structures.
Optionally, the micro-nano structure is covered on the photoelectric conversion unit in the target area in the photoelectric conversion layer; the target area is: and a region of the photoelectric conversion layer extending outward from a center point of the photoelectric conversion layer along a diagonal line of the photoelectric conversion layer by a specified length.
Optionally, the specified length is a preset number times the diagonal length of the photoelectric conversion layer, and the preset number is in the range of [0.5,0.8];
and/or the number of the groups of groups,
the ratio of the number of the photoelectric conversion units covering the micro-nano structure in the photoelectric conversion layer to the total number of the photoelectric conversion units in the photoelectric conversion layer is in the range of [1/1000,1].
Optionally, there is no space between each micro-nano structure covered on the photoelectric conversion layer.
Optionally, at least one group of micro-nano structures is covered on each group of photoelectric conversion units in the photoelectric conversion layer; the micro-nano structures covered on the same group of photoelectric conversion units are free from intervals, and the micro-nano structures in the same group are free from intervals;
Wherein, a group of photoelectric conversion units comprises N multiplied by N adjacent photoelectric conversion units; the micro-nano structure belonging to the same group has the same type and size; n is an integer greater than 1.
Alternatively, N may range from [3, 50].
Optionally, the intervals between the first micro-nano structures covered on the different groups of photoelectric conversion units increase along the direction of outwards extending the diagonal line of the photoelectric conversion layer from the center point of the photoelectric conversion layer; the first micro-nano structure is adjacent to micro-nano structures covered on other groups of photoelectric conversion units.
Optionally, the system further comprises: a microlens array MLA;
the MLA is arranged on the upper layer of the micro-nano structure; or,
the MLA is disposed between the micro-nano structure and the photoelectric conversion layer.
Optionally, the performing object recognition based on the first spectral image data and the second spectral image data includes:
according to the weights respectively corresponding to the first designated wavelength and the second designated wavelength, fusing the first spectrum image data and the second spectrum image data to obtain fused spectrum image data; generating a fusion image based on the spectral image data; performing skin texture recognition based on the fused image; and generating an infrared light image based on the second spectral image data, and performing living organism identification based on the infrared light image.
As can be seen from the above, in the image recognition system provided in the embodiments of the present application, a spectrum imager is introduced, and since the photoelectric conversion layer of the spectrum imager is covered with the micro-nano structure, the micro-nano structure can modulate the reflected light of the target object, and the reflected light is: light of a first specified wavelength from the visible light source is reflected by the target object and light of a second specified wavelength from the infrared light source is reflected by the target object. In this case, the target spectral image data output from the spectral imager includes not only the first spectral image data of the first specified wavelength but also the second spectral image data of the second specified wavelength. Based on the data, the processor can analyze the information related to the visible light, namely the surface texture of the target object, and the information related to the infrared light, namely the living body identification of the target object. And then, target recognition is carried out based on the analyzed first spectrum image data and the second spectrum image data, and a visible light image and a near infrared light image are not required to be acquired in a time-sharing way by switching optical filters, so that devices of an image recognition system can be reduced, the complexity of the image recognition system is reduced, and the time consumption of the image recognition system for acquiring images is reduced.
Of course, not all of the above-described advantages need be achieved simultaneously in practicing any one of the products or methods of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly introduce the drawings that are required to be used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other embodiments may also be obtained according to these drawings to those skilled in the art.
Fig. 1a is a block diagram of an image recognition system according to an embodiment of the present application;
fig. 1b is an application scenario diagram of an image recognition system provided in an embodiment of the present application;
FIG. 2 is a cross-sectional view of a first spectral imager provided in an embodiment of the present application;
FIG. 3 is a cross-sectional view of a second spectral imager provided in an embodiment of the present application;
FIG. 4 is a cross-sectional view of a third spectral imager provided in an embodiment of the present application;
FIG. 5 is a top view of a fourth spectral imager provided in an embodiment of the present application;
FIG. 6 is a top view of a fifth spectral imager provided in an embodiment of the present application;
fig. 7 is a flowchart of an image recognition method according to an embodiment of the present application;
FIG. 8 is a cross-sectional view of a generic imager provided in an embodiment of the present application;
fig. 9 is a cross-sectional view of a sixth spectral imager provided in an embodiment of the application.
11-lens; a 12-spectrum imager; 121-a photoelectric conversion layer; 1211-a photoelectric conversion unit; 122-micro-nano structure; 13-a processor; 141—a visible light source; 142-infrared light source.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. Based on the embodiments herein, a person of ordinary skill in the art would be able to obtain all other embodiments based on the disclosure herein, which are within the scope of the disclosure herein.
In the related art, since the monocular system includes only one lens, when the visible light image and the near infrared light image are collected in a time-sharing manner, the visible light image is generally collected by using the visible light filter, and then the visible light image is switched to the near infrared light filter to collect the near infrared light image. Therefore, the optical filter needs to be switched in the image acquisition process, so that the monocular system is complex in structure, and the time for acquiring the image is long due to the fact that the optical filter needs a certain time for switching.
In order to solve the above-mentioned problem, an embodiment of the present application provides an image recognition system, as shown in fig. 1a, the image recognition system includes: a lens 11, a spectral imager 12 and a processor 13. The photoelectric conversion layer 121 of the spectral imager 12 is covered with a micro-nanostructure 122. The micro-nano structure 122 is capable of modulating reflected light of a first specified wavelength and a second specified wavelength reflected by the target object. In this case, the target spectral image output from the spectral imager 12 includes not only the first spectral image data of the first specified wavelength but also the second spectral image data of the second specified wavelength. Based on this, the processor 13 can analyze the information related to the visible light, i.e., the surface texture of the target object, such as palm print and fingerprint, and the like, and the information related to the infrared light, i.e., the living body identification of the target object, such as blood vessel, and the like, based on the data of the target spectral image. And then, target recognition is carried out based on the analyzed first spectrum image data and the second spectrum image data, and a visible light image and an infrared light image are not required to be acquired in a time-sharing way by switching the optical filters, so that devices of an image recognition system can be reduced, the complexity of the image recognition system is reduced, and the time consumption of the image recognition system for acquiring images is reduced.
In one application scenario, the image recognition system is used for access control recognition of a target location, such as a school, company, district, etc. The method comprises the steps of collecting target spectrum image data of a target object through an image recognition system, and carrying out skin texture recognition and living organism recognition on the target object based on first spectrum image data and second spectrum image data which are analyzed from the target spectrum image data. Subsequently, if the identification result is that the skin texture identification and the living organism identification of the target object pass through the identification, the target object is determined to be allowed to enter the target place, and if the identification result is that any one of the skin texture identification and the living organism identification of the target object does not pass through the identification, alarm information is output so as to ensure the safety of the target place.
Referring to fig. 1a and 1b, an embodiment of the present application provides an image recognition system, including: a lens 11, a spectral imager 12 and a processor 13.
The target object is irradiated with light of a first specified wavelength emitted by the visible light source 141 and light of a second specified wavelength emitted by the infrared light source 142. The lens 11 is used for collecting the reflected light of the visible light source 141 and the infrared light source 142 from the target object. Wherein, the visible light source 141 is used for presenting the surface texture of the target object, and the infrared light source 142 is used for presenting the living body identification of the target object.
The photoelectric conversion layer 121 of the spectral imager 12 is covered with a micro-nanostructure 122. Micro-nano structure 122 for modulating the reflected light.
The photoelectric conversion layer 121 of the spectrum imager 12 is configured to convert optical signals and electrical data based on the light modulated by the micro-nano structure 122, and output target spectrum image data of a target object.
The processor 13 is configured to parse the first spectral image data of the first specified wavelength and the second spectral image data of the second specified wavelength from the target spectral image data based on the response coefficients of the micro-nano structure 122 to the first specified wavelength and the second specified wavelength, and perform target recognition based on the first spectral image data and the second spectral image data. Wherein the response coefficient is used to distinguish spectral image data of different wavelengths from spectral image data obtained by the photoelectric conversion layer 121.
According to the image recognition system provided in the embodiment of the present application, since the micro-nano structure 122 is covered on the photoelectric conversion layer 121 of the spectrum imager 12, and since the micro-nano structure 122 can modulate the reflected light of the target object, the reflected light is: light of a first specified wavelength from the visible light source 141 is directed to the target object for reflection and light of a second specified wavelength from the infrared light source 142 is directed to the target object for reflection. In this case, the target spectral image data output from the spectral imager 12 includes not only the first spectral image data of the first specified wavelength but also the second spectral image data of the second specified wavelength. Based on this, the processor 13 can analyze the information related to the visible light, i.e., the surface texture of the target object, and the information related to the infrared light, i.e., the living body identification of the target object, based on the data of the target spectral image. And then, target recognition is carried out based on the analyzed first spectrum image data and the second spectrum image data, and a visible light image and a near infrared light image are not required to be acquired in a time-sharing way by switching optical filters, so that devices of an image recognition system can be reduced, the complexity of the image recognition system is reduced, and the time consumption of the image recognition system for acquiring images is reduced. In addition, the image recognition system comprises the lens 11 and the spectrum imager 12, so that the complexity of the image recognition system can be further reduced, and the cost of the image recognition system can be reduced.
The target object may be a palm, finger, wrist, etc.
When the target object is a palm, the surface texture of the target object is a palm texture; when the target object is a finger, the surface texture of the target object is a fingerprint; when the target object is a wrist, the surface texture of the target object is the skin texture of the wrist.
The living body of the target object is identified as a vein. When the target object is a palm, the living body of the target object is marked as a palm vein; when the target object is a finger, the living body of the target object is identified as a finger vein; when the target object is a wrist, the living body of the target object is identified as a wrist vein.
In this embodiment, the target object is taken as a palm as an example, and for the implementation manner of other cases such as a finger, a wrist, etc., reference may be made to the implementation manner of the target object as a palm, and description thereof will not be repeated in this embodiment.
Palmprint is a variety of surface textures on the palm surface between the wrist and fingers, the specific arrangement and combination of which forms the unique features of palmprint. These features may be used for identity authentication. And the palm area is larger, the features that can be extracted by the palmprint are more, the contained information is more abundant, and the palmprint has the advantage of non-contact.
The band of palm print imaging is the visible light band, as shown in fig. 1b, with the palm illuminated with light of a first specified wavelength from the visible light source 141. The first designated wavelength may include a plurality of wavelengths within the visible light band. For example, the first specified wavelength may include 450nm, 550nm, 620nm, and the like in the visible light band.
The capillary vessel in the palm has stronger absorptivity to infrared light, so that the infrared image generated by the irradiation of infrared light to the palm can show darker vein lines at subcutaneous veins, and the identity authentication can be performed by using the special vein lines. And, the capillary vessel in palm is difficult to forge when the palm vein, and the palm vein has different absorption/reflection degrees to light of different wave bands, so that huge difference exists between spectral image data of the palm and the palm model of a real person, and the palm model of the real person can be distinguished based on the palm vein for biological living body identification, so that the accuracy of an image identification system is improved.
The imaging band of the palmar vein is an infrared band, as shown in fig. 1b, and the palm is illuminated with light of a second prescribed wavelength from an infrared light source 142. The second designated wavelength may include a plurality of wavelengths within the infrared band. The infrared light band includes: near infrared band, mid infrared band and far infrared band. For example, the second designated wavelength includes 750nm and 780nm in the near infrared band, and the like.
Accordingly, after the light with the first specified wavelength emitted by the visible light source 141 and the light with the second specified wavelength emitted by the infrared light source 142 are irradiated to the palm, part of the light is absorbed by the palm, and part of the light is reflected to form reflected light. The lens 11 can collect reflected light of the palm. The reflected light includes light of a first specified wavelength and light of a second specified wavelength. The lens 11 may be a normal optical lens. Alternatively, it may be a Metasurface lens.
Referring to fig. 2, the spectral imager 12 includes a photoelectric conversion layer 121 and a micro-nano structure 122. The micro-nano structure 122 covers the photoelectric conversion layer 121, and the micro-nano structure 122 is located on the photosensitive path of the photoelectric conversion layer 121.
The micro-nano structure 122 may modulate the reflected light, which refers to decomposing and resolving the reflected light.
After the light modulated by the micro-nano structure 122 reaches the photoelectric conversion layer 121, the photoelectric conversion layer 121 converts an optical signal and electrical data based on the light modulated by the micro-nano structure 122, and outputs target spectral image data of a target object.
The spectral imager 12 outputs target spectral image data based on a spectral imaging technique. The spectrum imaging technology is a novel multidimensional information acquisition technology combining the imaging technology and the spectrum technology, the spectrum imaging technology can detect and acquire two-dimensional space information and one-dimensional spectrum information of a measured target object to form spectrum image data of the target object, and the spectrum image data of a single channel under different wave bands of the target object can be acquired through analysis processing.
The plurality of micro-nano structures 122 overlaid on the spectral imager 12 are arranged in a periodic pattern. The plurality of micro-nano structures 122 constitute the supersurface of the spectral imager. The type of structure and the dimensions of the plurality of micro-nano structures 122 are determined based on the first specified wavelength and the second specified wavelength. The type of structure of micro-nano structure 122 may be represented using information such as material, shape, etc. The shape of micro-nano structure 122 includes a cylinder, a cross, a square column, etc.
The photoelectric conversion layer 121 includes a plurality of photoelectric conversion units 1211, and the photoelectric conversion units 1211 are PD (english: photo-Diode, chinese: photodiode).
Illustratively, n (e.g., 5) specified wavelengths are selected in the visible band and in the infrared band. For example, a first specified wavelength of 450nm, 550nm, and 620nm is selected in the visible light band, and a second specified wavelength of 750nm and 780nm is selected in the infrared light band. The particular micro-nano structure 122 is designed for the type of structure and size according to the selected first and second designated wavelengths. In the photoelectric conversion layer 121 including w×h photoelectric conversion units 1211, n×n photoelectric conversion units 1211 are grouped, and each group of photoelectric conversion units 1211 is covered with the designed micro-nano structure 122.
In some embodiments, the ratio of the number of photoelectric conversion units 1211 in the photoelectric conversion layer 121 covering the micro-nano structure 122 to the total number of all the photoelectric conversion units 1211 in the photoelectric conversion layer 121 ranges from [1/1000,1].
Specifically, the above ratio is set by the skilled person according to the total number of photoelectric conversion units 1211 and the actual demand. In some embodiments, where photoelectric conversion layer 121 includes 1280×960 photoelectric conversion units 1211, the above ratio ranges from [1/100,1]. For example, the above ratio is 1/100, etc.
For example, the photoelectric conversion layer 121 includes 1280×960 photoelectric conversion units 1211, where 5×5 photoelectric conversion units 1211 are a group (i.e., one spatial period), and the group of photoelectric conversion units 1211 is covered with the designed micro-nano structure 122 continuously without intervals.
Alternatively, a group of 5×5 photoelectric conversion units 1211 is formed, and the group of photoelectric conversion units 1211 is periodically covered with the designed micro-nano structure 122 at intervals. The number of photoelectric conversion units 1211 covering the micro-nano structure 122 may account for 1/100 of the total number of all photoelectric conversion units 1211.
In order to acquire the richer spectral image data of the target object in the visible light band and the infrared light band, each of the photoelectric conversion units 1211 in the photoelectric conversion layer 121 of the spectral imager 12 is covered with the micro-nano structure 122. The number of micro-nano structures 122 covered on one photoelectric conversion unit 1211 is determined based on the size of the micro-nano structures 122 and the size of the photoelectric conversion unit 1211. For example, referring to fig. 3, 4 micro-nano structures 122 are covered on each photoelectric conversion unit 1211.
In order to reduce the cost of the image recognition system, a part of the photoelectric conversion units 1211 of the photoelectric conversion layer 121 is covered with the micro-nano structure 122. For example, referring to fig. 4, the left 2 nd photoelectric conversion unit 1211, the left 4 th photoelectric conversion unit 1211, and the left 6 th photoelectric conversion unit 1211 in the photoelectric conversion layer 121 are covered with the micro-nano structure 122. The 1 st left photoelectric conversion unit 1211, the 3 rd left photoelectric conversion unit 1211, and the 5 th left photoelectric conversion unit 1211 do not cover the micro-nano structure 122.
In some embodiments, the area of the photoelectric conversion layer 121 not covered by the micro-nano structure 122 may be covered by a micro-lens array, a convex lens, a concave lens, a fresnel lens, or the like, for adjusting the reflected light collected by the lens 11.
In some embodiments, the micro-nano structure 122 is covered on the photoelectric conversion unit 1211 in the target region in the photoelectric conversion layer 121; the target area is: a region of the photoelectric conversion layer 121 having a specified length extends outward from a center point of the photoelectric conversion layer 121 along a diagonal line of the photoelectric conversion layer 121.
The designated length is determined by a technician according to the size of the target object, so that the complete spectrum image data of the target object can be obtained through the micro-nano structure 122, and the accuracy of the image recognition system is improved. For example, when the target object is a finger, the designated length may be set to a smaller value, and the volume of the spectral imager 12 may be reduced, thereby improving portability of the image recognition system. When the target object is a palm, a numerical value with a larger designated length can be set, spectral image data with more palms can be obtained, and the accuracy of the image recognition system is improved.
In some embodiments, the specified length is a preset number times the diagonal length of the photoelectric conversion layer 121, the preset number ranging from [0.5,0.8]. For example, the specified length is 0.7 times the length of the diagonal line of the photoelectric conversion layer 121, and in the case of acquiring spectral image data of the central region of the target object, the number of covered micro-nano structures 122 is reduced, reducing the cost of the image recognition system.
Referring to fig. 5, the target region is a region in which 5 photoelectric conversion units 1211 extend outward along the diagonal line of the photoelectric conversion layer 121 from the center point of the photoelectric conversion layer 121, and one photoelectric conversion unit 1211 within the target region is covered with 4 micro-nano structures 122.
The target area in the photoelectric conversion layer 121 covers the micro-nano structure 122, so that the number of the covered micro-nano structures 122 can be reduced, and the cost of the image recognition system can be further reduced. In addition, when the image recognition system is actually used for recognition, the target object is basically placed at a position corresponding to the central area of the spectrum imager 12, and the target area is located in the central area of the photoelectric conversion layer 121, that is, the central area of the photoelectric conversion layer 121 is covered with the micro-nano structure 122, so that spectrum image data of the central area of the complete target object can be obtained through the micro-nano structure 122, the information of the central area of the target object is more, and the accuracy of the image recognition system can be improved.
In some embodiments, there is no space between each micro-nano structure 122 covered on the photoelectric conversion layer 121. Accordingly, more micro-nano structures 122 can modulate the reflected light collected by the lens 11, so that the accuracy of the target spectrum image data of the photoelectric conversion layer 121 based on the light output modulated by the micro-nano structures 122 is improved, and the accuracy of the image recognition system is further improved.
For every two adjacent micro-nano structures 122, no space between the two micro-nano structures 122 means that no gap exists between the adjacent edges of the two micro-nano structures 122.
Since the material for manufacturing the micro-nano structure 122 is directly deposited on the photoelectric conversion layer 121 when the spectrum imager 12 is manufactured, and then etching is performed according to the structure type and size of the designed micro-nano structure 122, for each group of photoelectric conversion units 1211, each micro-nano structure 122 covered on the group of photoelectric conversion units 1211 is an integral unit, so that no gap between adjacent micro-nano structures 122 can be realized.
For example, in fig. 3, there is no space between the micro-nano structures 122 covered on the photoelectric conversion layer 121. There is no space between each micro-nano structure 122 covered on one photoelectric conversion unit 1211 in fig. 4. There is no space between the respective micro-nano structures 122 covered on the photoelectric conversion units 1211 in the target region of the photoelectric conversion layer 121 in fig. 5.
In some embodiments, each set of photoelectric conversion units 1211 in the photoelectric conversion layer 121 is covered with at least one set of micro-nano structures 122. There is no space between the micro-nano structures 122 of the same group covered on the same group of photoelectric conversion units 1211, and there is no space between the micro-nano structures 122 of the same group. A group of photoelectric conversion units 1211 includes n×n adjacent photoelectric conversion units 1211. The micro-nano structures 122 belonging to the same group are identical in structure type and size. N is an integer greater than 1.
In some embodiments, the range of N above is [3, 50].
Specifically, the value of N may be set according to the total number of photoelectric conversion units 1211 in the photoelectric conversion layer 121 and the actual requirement. In some embodiments, when photoelectric conversion layer 121 includes 1280×960 photoelectric conversion units 1211, the range of N is [3, 50]. For example, N is 5, or N is 10, etc.
For example, referring to fig. 6,3×3 photoelectric conversion units 1211 are grouped into one group, and 9 groups of micro-nano structures 122 are covered on the group of photoelectric conversion units 1211. Each set of micro-nano structures 122 includes 4 micro-nano structures 122. There is no space between the micro-nano structures 122 covered on each group of photoelectric conversion units 1211 and immediately after the same group of micro-nano structures 122. The 4 micro-nano structures 122 in a group are identical in structure type and size.
In some embodiments, the spacing between the first micro-nano structures 122 overlaid on different groups of the photoelectric conversion units 1211 is the same within the target region of the photoelectric conversion layer 121. Wherein the first micro-nano structure 122 is adjacent to the micro-nano structure 122 covered on the other group of photoelectric conversion units 1211.
In some embodiments, the intervals between the first micro-nano structures 122 covered on the different sets of photoelectric conversion units 1211 increase in a direction extending outward along the diagonal line of the photoelectric conversion layer 121 from the center point of the photoelectric conversion layer 121.
In actual use of the image recognition system, the target object is placed in a position corresponding to the central region of the spectral imager 12, and the central region of the spectral imager 12 collects spectral image data of the central region of the target object, and the edge region of the spectral imager 12 collects spectral image data of the edge region of the target object. And, the information of the edge area of the target object is less, so that the accuracy of the image recognition system is less affected.
The interval between the first micro-nano structures 122 increases in the direction in which the diagonal line of the photoelectric conversion layer 121 extends outward, that is, the interval between the first micro-nano structures 122 covered by the edge region of the spectrum imager 12 increases, and the first micro-nano structures 122 covered by the edge region of the spectrum imager 12 decreases. That is, the density of micro-nano structures 122 covered by the edge region of spectral imager 12 may be reduced, thereby reducing the cost of the image recognition system and ensuring the accuracy of the image recognition system.
In some embodiments, the image recognition system further comprises: MLA. (English: microlens Array, chinese: microlens Array). The MLA can diffuse and shape light entering the MLA, and can realize specific functions such as uniformity, focusing, modulation and the like of the light.
The MLA overlies the micro-nano structure 122. The positional relationship of the individual microlenses in the MLA to the individual micro-nano structures 122 may be set by the skilled artisan based on the requirements.
In preparing the spectral imager 12, the positions of the microlenses covering the central region of the micro-nanostructure 122 are determined, and then the positions of the microlenses covering the central region are shifted based on the requirement, so that the positions of the microlenses covering the microlenses can be obtained, and the arrangement mode of the microlenses can be obtained. Then, a layer of material for manufacturing the micro-lenses is deposited on the upper layer of the micro-nano structure 122, and the deposited material is etched according to a determined arrangement mode, so that the micro-nano structure 122 can be covered with the MLA.
Since the photoelectric conversion layer 121 is covered with the plurality of micro-nano structures 122, the central region of the micro-nano structures 122 refers to the region at the same position as the central region of the photoelectric conversion layer 121.
Alternatively, the MLA is disposed between the micro-nano structure 122 and the photoelectric conversion layer 121. That is, the MLA is covered on the upper layer of the photoelectric conversion layer 121, and the micro-nano structure 122 is covered on the upper layer of the MLA. When the MLA is covered on the upper layer of the photoelectric conversion layer 121, the focal points of the microlenses in the MLA are substantially located on the surface of the photoelectric conversion layer 121, and the positional relationship of the respective microlenses in the MLA and the respective photoelectric conversion units 1211 can be set by a technician on an as-needed basis. For example, each microlens in the MLA may correspond one-to-one to each photoelectric conversion unit 1211 in the photoelectric conversion layer 121.
When the spectral imager 12 is prepared, the positions of the microlenses covered on the central region of the photoelectric conversion layer 121 are determined first, and then the positions of the covered microlenses determined on the central region are shifted based on the requirement, so that the positions of the covered microlenses can be obtained, that is, the arrangement mode of the microlenses can be obtained. Then, a layer of material for manufacturing a microlens is deposited on the upper layer of the photoelectric conversion layer 121, and the deposited material is etched in a well-defined arrangement, so that the MLA can be covered on the photoelectric conversion layer 121. Then, a layer of material for manufacturing the micro-nano structure 122 is deposited on the upper layer of the MLA, and the deposited material is etched, so that the micro-nano structure 122 can be covered on the MLA.
In some embodiments, one photoelectric conversion unit 1211 in the spectrum imager 12 is a unit pixel, the photoelectric conversion unit 1211 outputs spectrum image data of the unit pixel, and the spectrum image data output by each photoelectric conversion unit 1211 is intensity information of light modulated by the micro-nano structure 122 detected by the photoelectric conversion unit 1211.
Also, since the micro-nano structure 122 is designed based on the first and second specified wavelengths, the micro-nano structure 122 can enhance the light of the first and second specified wavelengths among the reflected light when modulating the reflected light.
The processor 13 may be MCU (English: micro Control Unit, chinese: micro control unit), CPU (English: central Processing Unit, chinese: central processor), GPU (English: graphics Processing Unit, chinese: image processor), FPGA (English: field-Programmable Gate Array, chinese: field programmable gate array), NPU (English: neural-network Processing Unit, chinese: embedded Neural network processor), etc. The processor 13 may be electrically connected to the spectral imager 12, and the target spectral image data output by the spectral imager 12 may be imported to the processor 13 for processing.
Accordingly, the processor 13 may parse the first spectral image data of the first specified wavelength and the second spectral image data of the second specified wavelength from the target spectral image data based on the response coefficients of the micro-nano structure 122 to the first specified wavelength and the second specified wavelength. The response coefficient is used to distinguish spectral image data of different wavelengths from the spectral image data obtained by the photoelectric conversion layer 121.
For example, for each group of photoelectric conversion units 1211, the processor 13 may extract spectral image data of a specified wavelength corresponding to the group of photoelectric conversion units 1211 from the target spectral image data based on the following formula (1), that is, may obtain spectral image data of the target object at the specified wavelength.
（1）；
Wherein,the target spectral image data representing the nth unit pixel in the group of photoelectric conversion units 1211, N being the number of unit pixels in the group of photoelectric conversion units 1211. />Representing the response coefficient of the micro-nano structure 122 covered on the nth unit pixel in the group of photoelectric conversion units 1211 for the mth designated wavelength, for example, a +.>The micro-nano structure 122 overlaid on the 1 st unit pixel in the group of photoelectric conversion units 1211 has a response coefficient for the 1 st specified wavelength. The response coefficient is determined by two factors of the transmittance of the micro-nano structure 122 for a specified wavelength and the quantum efficiency of the response of the photoelectric conversion layer 121.Represents the Mth designated wavelength (i.e.)>) I.e., intensity information of the mth specified wavelength of light modulated by the micro-nano structure 122.
In practical application, the micro-nano junctionResponse coefficient of structure 122 for a specified wavelengthIt is known that the target spectral image data outputted based on the photoelectric conversion can be 121 +.>And (3) reversely pushing the formula (1) by utilizing an algorithm to obtain spectral image data with a specified wavelength.
When in formula (1)Representing the response coefficient of the micro-nano structure 122 covered on the nth unit pixel in the group of photoelectric conversion units 1211 for the mth first specified wavelength, the ∈k obtained based on the above formula (1) >To->First spectral image data for a first specified wavelength.
When in formula (1)The response coefficient of the micro-nano structure 122, which represents the coverage on the nth unit pixel in the group of photoelectric conversion units 1211, for the mth second specified wavelength is obtained based on the above formula (1)>To->And second spectral image data for a second specified wavelength.
Further, the processor 13 may perform object recognition based on the first spectral image data and the second spectral image data.
In one implementation, the processor 13 may further fuse the first spectral image data of each first specified wavelength, for example, calculate a weighted sum of the first spectral image data of each first specified wavelength, to obtain spectral image data of a visible light band.
The processor 13 may further fuse the second spectral image data of each second specified wavelength, for example, calculate a weighted sum of the second spectral image data of each second specified wavelength, to obtain spectral image data of an infrared light band.
Then, the processor 13 generates a visible light image based on the spectral image data of the visible light band, and the visible light image can present the surface texture of the target object, and then the skin texture recognition can be performed on the target object based on the visible light image. The processor 13 generates an infrared light image based on the spectral image data of the infrared light band, the infrared light image being used to present the living body identification of the target object, and performs living body identification of the target object based on the infrared light image. Thus, the surface texture of the target object and the biological characteristics of the living body mark are not interfered with each other, and the image recognition system can independently perform two kinds of recognition, so that the accuracy of image recognition is improved.
In another implementation, the processor 13 fuses the first spectral image data and the second spectral image data according to weights corresponding to the first specified wavelength and the second specified wavelength, to obtain fused spectral image data. Then, generating a fusion image based on the spectral image data; performing skin texture recognition based on the fused image; and generating an infrared light image based on the second spectral image data, and performing living organism identification based on the infrared light image.
For example, the processor 13 may obtain fused spectral image data based on the following formula (2).
（2）；
Wherein,representing fused spectral image data,/->Indicating the weight corresponding to the i-th designated wavelength. />Represents the j-th designated wavelength (i.e.)>) Is described.
The fused image obtained based on the fused spectral image data can form the surface texture of the unique target object. The fusion image is used for skin texture recognition, so that the accuracy of the image recognition system can be improved.
In some embodiments, the image recognition system further comprises a ranging unit. The ranging unit may be a proximity sensor. The ranging unit is used for detecting whether the target object is in the visual field range of the image recognition system. The image recognition system acquires target spectral image data of the target object when the target object is within a field of view of the image recognition system. When the target object is not within the field of view of the image recognition system, a corresponding signal may be output to the processor 13. The processor 13 may then output a reminder message prompting the movement of the target object into the field of view of the image recognition system.
In some embodiments, the image recognition system further comprises a wake-up unit, when the image recognition system is in a standby state, the wake-up unit activates the image recognition system to shift to a normal operation state if the target object is detected to be close to or in contact with the image recognition system. The wake-up unit can adopt an infrared correlation structure or a touch wake-up structure.
Referring to fig. 7, a flowchart of an image recognition method according to an embodiment of the present application is illustrated by taking a target object as a palm as an example. The image recognition method is applied to an image recognition system, and the image recognition system comprises: a lens 11, a spectral imager 12 and a processor 13.
S701: and acquiring palm reflected light, and performing spectral imaging.
In this step, the palm reflected light is reflected when the palm is irradiated by the light of the first specified wavelength emitted by the visible light source 141 and the light of the second specified wavelength emitted by the infrared light source 142, and the image recognition system performs spectral imaging based on the reflected light of the palm, so as to obtain target spectral image data of the palm.
S702: and (5) spectrum analysis.
In this step, the processor 13 in the image recognition system performs spectral analysis on the target spectral image data of the palm to obtain an image of the visible light channel (i.e., a visible light image) and an image of the near infrared light channel (i.e., an infrared image).
S703: and (5) palm print recognition.
In this step, the processor 13 performs palm print recognition on the palm based on the visible light image.
S704: and (5) palm vein identification.
In this step, the processor 13 performs palm vein recognition on the palm based on the infrared light image.
Based on the image recognition method provided by the embodiment of the application, the processor 13 performs palm print recognition on the palm based on the visible light image and palm vein recognition on the palm based on the infrared light image, the palm print and the palm vein are not interfered with each other, and the image recognition system can perform two kinds of recognition independently, so that the accuracy of image recognition is improved.
In some embodiments, referring to fig. 8, fig. 8 is a cross-sectional view of a generic imager provided in an embodiment of the present application. For example, CIS (English: complementary Metal-Oxide-Semiconductor Image Sensor, chinese: complementary metal Oxide integrated circuit imaging sensor), CCD (English: charge-Coupled Device, chinese: charge-Coupled Device), etc., the uppermost layer of a common imager is an MLA, which includes a plurality of microlenses. The next layer of MLA is CCD (English: color Filter Array, chinese: color filter array). The CFA includes a plurality of color filters arranged in a bayer array, i.e., RGB (english: red, green, blue, chinese: red, green, and blue) three-channel filters. The next layer of the CFA is a photoelectric conversion layer. A common imager obtains an image of RGB three-color channels based on a demosaicing interpolation algorithm, but during imaging, real color information of a band filtered by an optical filter is discarded.
And the photoelectric conversion layer 121 of the spectral imager 12 shown in fig. 9 is covered with a micro-nano structure 122. Compared with the prior art that the real color information of the wave band filtered by the optical filter is discarded in the imaging process by the common imager. Micro-nano structures 122 in spectral imager 12 are capable of modulating light of a first specified wavelength and a second specified wavelength to preserve true color information of the target object in the visible light band and the infrared light band. Subsequently, the processor 13 can improve the accuracy of the image recognition system when performing object recognition based on the first spectral image data of the first specified wavelength and the second spectral image data of the second specified wavelength.
In the technical scheme of the application, related operations such as acquisition, storage, use, processing, transmission, provision, disclosure and the like of spectrum image data containing surface texture and living body identification of a target object are all performed under the condition that user authorization is obtained.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer readable storage medium to another, for example, by wired (e.g., coaxial cable, fiber optic, DSL (digital subscriber line)) or wireless (e.g., infrared, wireless, microwave, etc.) means from one website, computer, server, or data center. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or an SSD (english: solid State Disk, chinese: solid State Disk), etc.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the present application. Any modifications, equivalent substitutions, improvements, etc. that are within the spirit and principles of the present application are intended to be included within the scope of the present application.
Claims (10)
1. An image recognition system, the image recognition system comprising: a lens, a spectral imager, and a processor;
the method comprises the steps that light with a first specified wavelength emitted by a visible light source and light with a second specified wavelength emitted by an infrared light source are used for irradiating a target object, the lens is used for collecting reflected light of the target object on the visible light source and the infrared light source, wherein the visible light source is used for presenting surface grains of the target object, and the infrared light source is used for presenting living body marks of the target object;
the photoelectric conversion layer of the spectrum imager is covered with a micro-nano structure, and the micro-nano structure is used for modulating the reflected light;
the photoelectric conversion layer of the spectrum imager is used for converting optical signals and electric data based on the light modulated by the micro-nano structure and outputting target spectrum image data of the target object;
the processor is used for analyzing first spectrum image data of the first specified wavelength and second spectrum image data of the second specified wavelength from the target spectrum image data based on response coefficients of the micro-nano structure to the first specified wavelength and the second specified wavelength, and performing target identification based on the first spectrum image data and the second spectrum image data; wherein the response coefficient is used for distinguishing spectral image data of different wavelengths from target spectral image data obtained by the photoelectric conversion layer.
2. The image recognition system of claim 1, wherein the image recognition system,
each photoelectric conversion unit in the photoelectric conversion layer is covered with a micro-nano structure; or,
and part of the photoelectric conversion units in the photoelectric conversion layer are covered with micro-nano structures.
3. The image recognition system according to claim 2, wherein the photoelectric conversion unit in the target area in the photoelectric conversion layer is covered with a micro-nano structure; the target area is: and a region of the photoelectric conversion layer extending outward from a center point of the photoelectric conversion layer along a diagonal line of the photoelectric conversion layer by a specified length.
4. The image recognition system according to claim 3, wherein the specified length is a preset number times a diagonal length of the photoelectric conversion layer, the preset number ranging from [0.5,0.8];
and/or the number of the groups of groups,
the ratio of the number of the photoelectric conversion units covering the micro-nano structure in the photoelectric conversion layer to the total number of the photoelectric conversion units in the photoelectric conversion layer is in the range of [1/1000,1].
5. An image recognition system according to claim 2 or 3, wherein,
and no space exists between each micro-nano structure covered on the photoelectric conversion layer.
6. The image recognition system of claim 5, wherein the image recognition system,
each group of photoelectric conversion units in the photoelectric conversion layer is covered with at least one group of micro-nano structure; the micro-nano structures covered on the same group of photoelectric conversion units are free from intervals, and the micro-nano structures in the same group of micro-nano structures are free from intervals;
wherein, a group of photoelectric conversion units comprises N multiplied by N adjacent photoelectric conversion units; n is an integer greater than 1; the micro-nano structures belonging to the same group are the same in type and size.
7. The image recognition system of claim 6, wherein N ranges from [3, 50].
8. The image recognition system of claim 6, wherein the image recognition system,
the intervals among the first micro-nano structures covered on different groups of photoelectric conversion units are increased along the direction of outwards extending the diagonal line of the photoelectric conversion layer from the center point of the photoelectric conversion layer; the first micro-nano structure is adjacent to micro-nano structures covered on other groups of photoelectric conversion units.
9. The image recognition system of claim 1, wherein the system further comprises: a microlens array MLA;
the MLA is arranged on the upper layer of the micro-nano structure; or,
The MLA is disposed between the micro-nano structure and the photoelectric conversion layer.
10. The image recognition system of claim 1, wherein the performing object recognition based on the first spectral image data and the second spectral image data comprises:
according to the weights respectively corresponding to the first designated wavelength and the second designated wavelength, fusing the first spectrum image data and the second spectrum image data to obtain fused spectrum image data; generating a fused image based on the fused spectral image data; performing skin texture recognition based on the fused image; and generating an infrared light image based on the second spectral image data, and performing living organism identification based on the infrared light image.
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