Method and device for calibrating a camera used in optical recognition

Abstract

The present invention relates to a method for calibrating mobile terminals of the same model equipped with a camera used in a non-contact optical recognition process, characterized in that it comprises the following steps: - obtaining a first plurality of subject images captured by terminals of the same terminal model, each image being associated with a focus value used during capture; - for each image, determining a subject resolution in the image from the known size of the subject and the subject size in pixels in the image; - estimating a first relationship between the focus value and the subject resolution in the image by regression on a set of points, each point corresponding to the pair focus value and subject resolution in the image for a given image; - transmitting the first estimated relationship to the terminals of the same model. [Fig. 3]

Description

Title of the invention: Method and device for calibrating a camera used in optical recognition

[0001] The invention relates to optical recognition systems using cameras. Optical recognition can involve the optical recognition of a user, through fingerprint recognition, palm print recognition, or facial recognition. It can also involve the optical recognition of a document, such as an official identity document.

[0002] In the field of image acquisition of fingerprints, palm prints, faces, or documents for optical character recognition (OCR), we need to obtain images for which the print resolution is known and sufficiently precise. Resolution is defined as the number of dots in the captured image per unit length of the captured object. This is also referred to as the subject resolution in the image. For example, resolution can be expressed in dots per inch (DPI) or dots per centimeter (DPC). This resolution should not be confused with the resolution of a printed image, which is expressed as the number of dots per unit length of the printed image. When the sensor used to capture the image operates in contact with the object, the resolution is known by the acquisition system.The sensors used in contact fingerprint capture systems obtain images with a resolution typically between 500 DPI or 1000 DPI.

[0003] Due to the proliferation of mobile devices such as smartphones and tablets, which are generally equipped with high-quality cameras, optical character recognition (OCR) systems are tending to replace dedicated contact sensors with these devices. The camera systems on these devices require the captured object to be at least a few centimeters from the camera lens to achieve focus and thus a clear image. The precise distance between the subject and the camera lens is not controlled. However, the resolution of the resulting image depends on this distance and decreases with increasing distance from the subject. These OCR systems are generally deployed on a very large scale, with each user using their own device to authenticate themselves, typically with a service.

[0004] These optical character recognition systems can be used to secure access to online services and buildings. They can also secure transactions. The specific use of the optical character recognition system and the interactions between it and the service whose access it secures are not the subject of this document.

[0005] Optical character recognition (OCR) algorithms used to authenticate the subject require a minimum resolution to ensure their proper functioning. We have seen that, in the case of fingerprints, a minimum resolution of 500 DPI is desirable, for example. The actual resolution thresholds to be met depend on the subject category—fingerprint, palm print, face, or document—but also on the OCR algorithms used.

[0006] These optical character recognition systems are subject to fraud attempts aimed at authenticating a representation of the subject as the original subject. This representation may, for example, be a photograph of the subject presented to the camera. This representation is rarely to scale with the subject. Typically, the representation is larger than the original in an attempt to better depict the details in the hope of deceiving the optical character recognition system.

[0007] Detecting this type of fraud improves the reliability of optical character recognition.

[0008] The invention presented aims to solve this problem. Description of the invention

[0009] To this end, the invention proposes a terminal model calibration method for establishing a relationship between focus values, i.e., the camera's focusing distance, and the subject's resolution in the image. The focus value is typically part of the metadata associated with the captured image. The size of the subject, such as a fingerprint or a document, while potentially varying slightly from one subject to another, is sufficiently fixed for a given type of subject to allow this relationship to be established statistically on a set of image captures of the subject. In the case of facial recognition, the distance between the eyes is used as the subject size. This distance is sufficiently fixed for the application's needs, the average distance between the eyes being 65 mm for an adult.Once this relationship is established, it becomes possible, during a new image capture of a subject for optical character recognition, to compare the expected resolution obtained through this relationship with the measured resolution in the subject's image. A significant difference between this expected resolution and the measured resolution strongly suggests attempted fraud.

[0010] A calibration method for mobile terminals of the same model equipped with a camera used in a contactless optical recognition method is thus proposed, characterized in that it comprises the following steps: - obtaining a first plurality of images of subjects captured by terminals of the same terminal model, each image being associated with a focus value used during capture; - for each image, determination of a resolution of the subject in the image from the known size of the subject and the size in pixels of the subject in the image; - estimation of a first relationship between the focus value and the resolution of the subject in the image by regression on a set of points, each point corresponding to the pair focus value and resolution of the subject in the image for an image obtained; - transmission of the first estimated relationship to the terminals of the same model.

[0011] In some embodiments, the step of estimating a relationship is executed when the number of images obtained is greater than a predetermined threshold.

[0012] In some embodiments, the transmission step is executed when a correlation coefficient obtained during the regression is greater than a predetermined threshold.

[0013] In some embodiments, the step of estimating a relationship is followed by a step of calculating the distance of each point to the curve obtained and a step of estimating a new relationship carried out by excluding the points furthest from the curve obtained during the first estimation.

[0014] In certain embodiments, the process comprises the following steps after the first relation estimation: - obtaining a second plurality of subject images captured by terminals of the same terminal model, each image being associated with a focus value used during capture; - for each image of the second plurality of images, determination of a resolution of the subject in the image from the known size of the subject and the size in pixels of the subject in the image; - estimation of a second relationship between the focus value and the resolution of the subject in the image by regression on a set of points, each point corresponding to the pair focus value and resolution of the subject in the image for an image obtained belonging to the first or second plurality of images; - transmission of the second estimated relationship to the terminals of the same model if the difference between the first relationship and the second relationship is greater than a predetermined threshold.

[0015] In some embodiments, the step of obtaining a first plurality of images includes for each image a step of detecting a type of subject represented in the image.

[0016] In some embodiments, the type of subject is one of the following types: - finger, - palm of the hand, - face, - document.

[0017] An optical subject recognition method is also proposed, comprising, by a terminal comprising a camera, the following steps: - capture of an image of the subject associated with the focus value used during the capture; - optical recognition of the subject; characterized in that it comprises the following steps: - obtaining a relationship between the focus value and the resolution of the subject in the image; - determination of a measured resolution of the subject in the image from a known size of the subject and the size in pixels of the subject in the image; - determination of an expected resolution of the subject in the image determined from the focus value and the relationship obtained; - rejection of the captured image due to suspected fraud when the measured resolution and the expected resolution differ by a value greater than a predetermined threshold.

[0018] In some embodiments, the method comprises the following steps: - determination of a resolution range of the subject in the image relating to the optical recognition of the subject; - determination of the corresponding range of focus values ​​based on the relationship; - determining an indication to move the subject closer or further away during capture when the focus value is outside the range; and - display of the indication when the image is captured.

[0019] A computer program product is also proposed comprising instructions for implementing the process according to the invention, when this program is executed by a processor.

[0020] A non-transient recording medium readable by a computer is also proposed on which a program is recorded for the implementation of the method according to the invention when this program is executed by a processor.

[0021] A device for calibrating terminals of the same model equipped with a camera used in an optical recognition process is also proposed, characterized in that it comprises a processor configured to execute the following steps: - obtaining a first plurality of images of subjects captured by terminals of the same terminal model, each image being associated with a focus value used during capture; - for each image, determination of a resolution of the subject in the image from the known size of the subject and the size in pixels of the subject in the image; - estimation of a first relationship between the focus value and the resolution of the subject in the image by regression on a set of points, each point corresponding to the pair focus value and resolution of the subject in the image for an image obtained; - transmission of the first estimated relationship to the terminals of the same model.

[0022] A mobile terminal for contactless optical recognition of a subject is also proposed, the terminal comprising a camera and a processor configured to execute the following steps: - capture of an image of the subject associated with the focus value used during the capture; - optical recognition of the subject; characterized in that it comprises the following steps: - obtaining a relationship between the focus value and the resolution of the subject in the image; - determination of a measured resolution of the subject in the image from a known size of the subject and the size in pixels of the subject in the image; - determination of an expected resolution of the subject in the image determined from the focus value and the relationship obtained; - rejection of the captured image when the measured resolution and the expected resolution differ by a value greater than a predetermined threshold.

[0023] The present invention also relates to a computer program comprising instructions for implementing the process described above, when this program is executed by a processor.

[0024] This program may use any programming language (for example, an object-oriented language or other), and may be in the form of interpretable source code, partially compiled code or fully compiled code.

[0025] Another aspect relates to a non-transient storage medium for a computer-executable program, comprising a set of data representing one or more programs, said one or more programs comprising instructions for, when said one or more programs are executed by a computer comprising a processing unit operationally coupled to memory means and an input / output interface module, to execute all or part of the process described above. Brief description of the drawings

[0026] Other features, details, and advantages of the invention will become apparent from the detailed description below. This description is purely illustrative and should be read in conjunction with the accompanying drawings, in which: Fig. 1

[0027] [Fig.1] illustrates the system for capturing a subject by an optical recognition application running on a mobile terminal; Fig. 2

[0028] [Fig.2] illustrates the architecture of an optical recognition system in embodiments of the invention; Fig. 3

[0029] [Fig.3] illustrates the main steps of a calibration process according to one embodiment of the invention; Fig. 4

[0030] [Fig.4] illustrates the main steps of the calibration process and its use by the terminal in embodiments of the invention; Fig. 5

[0031] [Fig.5] is a schematic block diagram of an information processing device for the implementation of one or more embodiments of the invention. Detailed description

[0032] Fig. 1 illustrates the system for capturing a subject 102 by an optical recognition application running on a mobile terminal 100.

[0033] The mobile terminal 100 is typically a smartphone, a digital tablet or any type of computer terminal equipped with a processor capable of executing a computer program and a camera 101 capable of taking an image of a subject 102. The terminal must also be equipped with means of communication with a data communication network.

[0034] The terminals operating within the optical recognition system can be highly varied. However, each terminal corresponds to a terminal model. A terminal model is therefore associated with a well-defined terminal model and a well-defined camera type. The behavior of the capture system is thus considered uniform for a given terminal model, despite possible slight variations due to the inevitable vagaries of the manufacturing process. A terminal model is identified by a model identifier, which is generally inserted into the metadata associated with each image captured by the terminal. It is therefore possible to determine the terminal model, and thus the camera model, used to capture an image from the metadata associated with that image, when such metadata exists.This model identifier is also stored by the terminal's operating system and accessible to applications running on the terminal, and more specifically to the optical character recognition application detailed below. The model of... terminal can therefore be obtained either from the image metadata or by the application from the operating system (for example via the camera API).

[0035] The camera 101 consists of a photosensitive sensor and a lens. It is an autofocus camera. This means that, depending on the distance 103 between the lens and the subject 102, the camera is able to automatically focus on the subject 102. This focusing is represented by a focus value, which depends directly on the distance 103 between the lens and the subject 102. The focus value is therefore representative of the distance 103 between the camera lens and the subject 102.

[0036] Subject 102 illustrated in the figure is a finger whose image is used for fingerprint recognition. The system is similar in the case of a palm for palm recognition, a face for facial recognition, or a document for official document recognition such as an identity document.

[0037] Figure [Fig.2] illustrates the architecture of an optical recognition system in embodiments of the invention.

[0038] The optical recognition system is controlled by an optical recognition software service 201 hosted by a remote and centralized platform 200. The platform 200 is connected to a data communication network, typically the Internet. The platform 200 may physically consist of one or more interconnected servers forming a server cluster, depending on the resources required by the software service 201.

[0039] The optical recognition system also comprises a set of mobile terminals 210-230. These mobile terminals are of different models. They respectively host the mobile applications 211-231 responsible for controlling optical recognition on the terminals. These mobile applications 211-231 are, in particular, responsible for managing the capture of images of the subject for optical recognition purposes.

[0040] Mobile terminals 210-230 are also connected to the data communication network, typically via a mobile phone network. Thus, mobile applications 211-231 are able to communicate with the optical recognition service 201 in order to exchange data with it.

[0041] The potential exchanges between the mobile applications 211-231 and the optical recognition service 201 related to the optical recognition itself are not described in this document. We describe here only the exchanges related to the calibration process and its use for fraud detection as detailed as described below. In the embodiment described, the optical recognition service 201 is also responsible for calibrating the terminals 210-230. In other embodiments, calibration can be handled by a calibration service separate from the optical recognition service 201. This calibration service can be hosted by the platform 200 or by another similar platform connected to the data communication network. When the optical recognition service and the calibration service are separate, the calibration steps described in this document as being performed by the optical recognition service are performed by the calibration service. The images required for calibration are then transmitted to the calibration service by the terminals, which implies that the terminals transmit their images to both services.Alternatively, the optical character recognition service processes the images, including instantaneously, using the calibration information it has stored and transmits the images received from the terminals to the calibration service. In both cases, the calibration service is centralized and receives images from the various terminals, allowing it to collect as much data as possible from each terminal.

[0042] In this method, during each image capture for optical character recognition (OCR) purposes taken by a terminal, the mobile application transmits to the OCR service 201 at least the captured image of the subject, the terminal model identifier, and the focus value used during capture. The terminal model identifier and the focus value are typically embedded in the metadata associated with the image. When this is not the case, they may be transmitted along with the image.

[0043] The optical recognition service 201 receives these images associated with a terminal model identifier and a focus value. The optical recognition service is responsible for estimating calibration values ​​based on information received from mobile applications. This estimation is performed per terminal model. For each terminal model, the optical recognition service collects information from the various terminals in that domain, and when it has received sufficient information, it estimates the calibration information for the terminal model in question. This calibration information is then transmitted by the optical recognition service to the terminals of the relevant model.

[0044] These exchanges are not necessarily synchronous. The terminals do not need to be connected to the optical recognition service when the image is captured. For example, the terminal may be out of range of the communication network when the image is captured. In this case, the terminal stores the information to be transmitted and sends it to the optical recognition service upon its next reconnection. to the network. Similarly, the optical recognition service can wait for a terminal's first reconnection to the network before transmitting the calibration information. In the preferred embodiment of the invention, the mobile application requests calibration information from the optical recognition service, for example, upon launch. In response to this request, the optical recognition service transmits the calibration information if it is available. When the calibration information is not available, the response to the request transmits information indicating this unavailability of the calibration information.

[0045] The calibration information is stored by the recognition service. Thus, when a new terminal connects to the recognition service for the first time, and when the calibration information has been estimated for the terminal model, this calibration information can be transmitted to it.

[0046] In an embodiment in which the optical recognition service and the calibration service are separate, the calibration information is stored by the calibration service. If the terminal is out of range of the communication network during image acquisition, the terminal stores the information to be transmitted and sends it to the calibration service upon its next reconnection to the network.

[0047] Figure 3 illustrates the main steps of a calibration process according to one embodiment of the invention.

[0048] The calibration process is typically performed by the optical recognition application 201. Alternatively, this process can be implemented by a dedicated calibration service separate from the optical recognition service as described above. It aims to estimate a relationship between the focus value and the resolution of the subject in the resulting image. It is recalled here that resolution is the resolution relative to the size of the subject. This relationship, once obtained, constitutes the calibration data.

[0049] In a first step S301, the optical recognition service receives from the terminal, in particular from the terminal's mobile application, an image of the subject captured during optical recognition, associated with the terminal model identifier and the associated focus value. This information is communicated to the calibration service for processing when the latter is separated from the optical recognition service.

[0050] During an S302 step, the corresponding point is calculated. The point is defined in two-dimensional space with focus values ​​on the x-axis and resolution relative to the subject on the y-axis. The physical size of the subject is known. For example, if the subject is a finger in the context of fingerprint recognition, the The size can correspond to the average width of a human finger. This value is a predefined constant of the process, for example stored in a table.

[0051] The size of the captured image is assumed here to be identical for all terminals of a particular model. Indeed, the size of the captured image is controlled by the mobile application during image capture, which may impose a fixed size for all terminals. In some embodiments, this size may vary; these differences must then be taken into account in the calculation. It is then possible to normalize this size, that is, to resize the received images to the same size for performing the calculations.

[0052] The optical recognition service then detects the subject in the received image. This detection can be performed by any object detection algorithm known to those skilled in the art. For example, the YOLO algorithm (for "You Only Look Once"). Once the subject is identified in the image, its type is determined and its size extracted from the table. The subject type is typically known, as recognition applications are typically dedicated to a single type of subject. Alternatively, the subject type can be determined by a classification algorithm if the application can process several subject types. This determination of the subject type can also be made by the terminal and the subject type information transmitted along with the image. The optical recognition service then measures its size in the image, expressed in pixels.For example, in the case of a finger, the optical character recognition service will measure the width of the finger in pixels in the image.

[0053] The ratio between the size of the subject measured in pixels in the image and the known size of the subject corresponds to the resolution of the subject in the received image.

[0054] The pair consisting of the focus value and the resolution of the subject in the image constitutes the point associated with this image.

[0055] During step S303, the calculated point is stored in association with the relevant terminal model. The optical recognition service therefore accumulates in memory the points calculated for each image received by each relevant terminal model.

[0056] During step S304, it is tested whether the number of points accumulated for the relevant terminal model is sufficient. This number of accumulated points is compared to a predetermined threshold. This predetermined threshold may depend on the type of subject and the terminal model. For example, in the case of documents, the size of the subject is known precisely, whereas the size of a finger is less precise; it may therefore be necessary to accumulate more points for a finger than for a document. The number of points must be sufficient to allow estimation of the relationship between the focus value and the resolution of the subject in the image. In the embodiment example, the subject is a finger, and the threshold values ​​used are, for example, values ​​between 100 and 1000.

[0057] As long as the sufficient number of points is not reached, the optical recognition service continues the accumulation by returning to step S301.

[0058] When a sufficient number of accumulated points are available, the S305 regression step is performed. This step aims to estimate the relationship between the focus value and the resolution of the subject in the image. It therefore involves estimating the equation of the mean curve representing the accumulated point cloud. This equation is, for example, polynomial, in which case this step consists of a polynomial regression step well known to those skilled in the art. Alternatively, a non-parametric regression using kernel estimation, also known as the Parzen-Rosenblatt method, can be used.

[0059] In one embodiment of the invention, the equation is assumed to be linear, which is a special case of a polynomial equation. The regression is therefore linear and allows the equation of the line representing the points accumulated in the two-dimensional space of the focus values ​​and resolutions of the subject in the image to be estimated.

[0060] During this initial calibration phase, fraud detection as described below is not activated. Therefore, some of the images obtained in step S301 may be the result of an attempted fraud. These images will typically generate points that can be described as outliers, which will deviate from the curve representing points from non-fraudulent images. By choosing a sufficiently high threshold for the number of points, the number of outliers remains low enough not to significantly alter the relationship estimate.

[0061] In certain embodiments of the invention, a new regression is performed without the points whose distance to the curve obtained during the first regression is greater than a threshold. This prevents these outliers from distorting the estimation.

[0062] Once the relationship has been estimated, this relationship, corresponding to the calibration result, is transmitted to all terminals of the relevant model during step S307. This transmission can take different forms depending on the embodiment. The calibration information can be integrated into a mobile application update, with the transmission then occurring during the mobile application update. Alternatively, the mobile application sends a request for the calibration information, for example, when accessing the service. According to another embodiment, the terminals do not store the calibration information and download it with each image capture.

[0063] In some embodiments, an additional step S306 is inserted before transmission. During this step S306, additional conditions are tested before validating the calibration. For example, a correlation coefficient can be determined during the regression. This correlation coefficient is representative of the standard deviation of the points from the estimated curve. As long as this correlation coefficient is less than a predetermined threshold, for example 0.95, the accumulation continues and we return to step S301. Only when the correlation coefficient becomes greater than this threshold is the calibration validated and the relationship passed to step S307.

[0064] Once the relationship has been transmitted to the terminals, or made available to them, the calibration is complete.

[0065] The method described here essentially considers four different types of subjects: the finger for fingerprint recognition, the palm for palm recognition, the subject's face, and official documents. The calibration process is performed for only one type of subject. In the case of a single application capable of recognizing more than one type of subject, the data is accumulated and processed by subject type according to a first embodiment. In other embodiments, the calibration incorporates images of different types of subjects. In this case, provided that the known size of the subject used in the calibration process is indeed the known size for the type of subject represented in each image, the process functions in the same way.

[0066] One of the important parameters of the process is the assumed known size of the subject. In the case of official documents, since these are standardized, the size is indeed known precisely. In the case of a finger or palm, the size used is a standard average size. This size, however, varies slightly within the population. In the case of facial recognition, the size used is the distance between the eyes. This variability leads to a loss of precision in the calibration method without, however, calling into question its relevance. In some embodiments, the type of subject can be divided into subtypes in the case of a finger, face, or palm. For example, it is possible to create a subtype for each age group and to associate each age group with a different standard finger or palm size.In facial recognition, an estimator can also be used that provides an estimate of the distance between the eyes based on the shape of the face. In the case of documents, a subtype can also be used; for example, the size of an identity card can vary depending on the country. The expected subject size used in the process then depends on the subject subtype.

[0067] In some embodiments, data from all subtypes are used for calibration. In other embodiments, calibration is performed for each subject subtype.

[0068] In some embodiments, once this initial calibration is completed, a continuous calibration adaptation process is performed. This process is similar to The initial calibration procedure is performed with the following differences. Step S304 is eliminated because the number of accumulated points is now always sufficient. The condition for step S306 is modified. In step S306, it is then tested whether the obtained relationship differs from the current relationship by a value greater than a predetermined threshold, for example, 5%.

[0069] When this is the case, the new estimate of the relationship, assumed to be more relevant because it is estimated on a larger number of points, is stored, in particular in the server of platform 200 of the recognition service, and transmitted to the terminals of the model concerned to be used as the result of the correlation. It becomes the new current relationship.

[0070] Thus, the calibration can be continuously refined.

[0071] Figure 4 illustrates the main steps of the calibration process and its use. by the terminal in embodiments of the invention.

[0072] During step S401, the terminal performs optical recognition under the control of the mobile application. This optical recognition step involves capturing an image of the subject. The processing of the captured image for optical recognition can be carried out by the terminal, by the optical recognition service after transmission during step S402, or in a distributed manner, with some steps performed by the terminal and others by the optical recognition service.

[0073] During step S402, the terminal transmits the captured image associated with the terminal model identifier and the focus value used to the optical recognition service.

[0074] These steps S401 and S402 can be executed in a loop depending on the use made by the terminal user of the optical recognition function.

[0075] During step S403, the terminal receives calibration information, i.e., the estimated relationship between the focus values ​​and the resolution of the subject in the image. This step can occur before any execution of steps S401 and S402, for example, in the case of a terminal activating optical recognition when that terminal model has already been calibrated by the optical recognition service. For example, the terminal initiating an initial capture connects to the recognition service, transmits its terminal model, and requests the corresponding calibration information. If the calibration information is available for that terminal model, the service transmits it in response to the request. In this embodiment, step S403 can be viewed as a substep of step S401.

[0076] In some embodiments, receiving the relationship triggers an S404 step for configuring a user guide for the optical recognition system. Indeed, a minimum resolution is generally required for proper operation. Optical recognition operation. Optionally, a maximum resolution can also be set. Using the relationship obtained in step S403, it is possible to determine the focus values ​​corresponding to these minimum and, if applicable, maximum resolutions. This allows a range of focus values ​​to be determined for use in optical recognition step S401 based on the relationship. During subsequent optical recognition steps S401, an indication prompting the user to move the subject closer to or further from the lens during capture is generated. This indication can be displayed on the terminal screen. This indication can be displayed until an image with a focus value within the desired range is obtained.

[0077] The relation reception step S403 also triggers the fraud detection setup step S405. This is because the relation can be used by the optical recognition step S401 to compare the subject resolution in the image, as measured in the obtained image, with the subject resolution in the expected image. The subject resolution in the expected image is the subject resolution in the image as given by the received relation, based on the focus value associated with the captured image. When the difference between these measured and expected resolutions exceeds a predetermined threshold, for example, 10%, fraud is suspected. Typically, this suspicion of fraud causes the optical recognition step S401 or any other appropriate processing to fail.

[0078] Alternatively, in embodiments where optical recognition is done by the optical recognition service, in particular via a terminal browser, fraud detection is configured and carried out on the platform by the optical recognition service.

[0079] Advantageously, captured images for which fraud is suspected are not transmitted to the optical character recognition (OCR) service. Alternatively, they are transmitted along with an indication of the suspected fraud. Advantageously, the focus and measured resolution information are also transmitted with the image and the indication of suspected fraud to allow for traceability of fraud detections. Advantageously, when continuous calibration adjustment is performed, these fraudulent images are not then used by the calibration process.

[0080] Figure [Fig. 5] is a schematic block diagram of a processing device Information 500 for the implementation of one or more embodiments of the invention. Device 500 may correspond to a mobile terminal 210-230 or to a server of the platform 200. The information processing device 500 may be a peripheral such as a microcomputer, a workstation or a mobile telecommunications terminal. The 500 device includes a communication bus connected to:

[0081] - a central processing unit 501, such as a microprocessor, denoted CPU;

[0082] - a 502 random access memory, denoted RAM, for storing the executable code of the method for implementing the invention, as well as registers adapted to record variables and parameters necessary for implementing the method according to embodiments of the invention; the memory capacity of the device can be supplemented by optional RAM connected to an expansion port, for example:

[0083] - a read-only memory 503, denoted ROM, for storing computer programs for the implementation of the embodiments of the invention;

[0084] - a 504 network interface is normally connected to a communication network on which digital data to be processed is transmitted or received. The 504 network interface can be a single network interface, or composed of a set of different network interfaces (e.g., wired and wireless, interfaces, or different types of wired or wireless interfaces). Data packets are sent on the network interface for transmission or are read from the network interface for reception under the control of the software application running in the 501 processor;

[0085] - a 505 user interface for receiving input from a user or for display information to a user;

[0086] - a storage device 506 as described in the invention and noted HD;

[0087] - a 507 input / output module for receiving / sending data to / from internal or external devices such as hard drives, cameras, removable storage media or others.

[0088] The executable code can be stored in read-only memory 503, on the storage device 506, or on a removable digital medium such as, for example, a disk. According to one embodiment, the executable code of the programs can be received via a communication network, through the network interface 504, in order to be stored in one of the storage means of the communication device 500, such as the storage device 506, before being executed.

[0089] The central processing unit 501 is adapted to command and direct the execution of instructions or portions of software code of the program or programs according to one of the embodiments of the invention, instructions which are stored in one of the aforementioned storage means. After power-up, the CPU 501 is capable of executing instructions from the main RAM 502, relating to a software application. Such software, when executed by the processor 501, causes the execution of the processes described.

[0090] In this embodiment, the device is a programmable device that uses software to implement the invention. However, alternatively, the present invention can be implemented in hardware (for example, in the form of a specific integrated circuit or ASIC).

[0091] Naturally, to satisfy specific needs, a person competent in the field of the invention may apply modifications to the preceding description.

[0092] Although the present invention has been described above with reference to specific embodiments, the present invention is not limited to specific embodiments, and modifications which fall within the scope of the present invention will be obvious to a person versed in the art.

Claims

Demands

1. A method for calibrating mobile terminals of the same model equipped with a camera used in a non-contact optical recognition method, characterized in that it comprises the following steps: - obtaining a first plurality of subject images captured by terminals of the same terminal model, each image being associated with a focus value used during capture; - for each image, determining a resolution of the subject in the image from the known size of the subject and the size in pixels of the subject in the image; - estimating a first relationship between the focus value and the resolution of the subject in the image by regression on a set of points, each point corresponding to the pair focus value and resolution of the subject in the image for a given image; - transmitting the first estimated relationship to the terminals of the same model.

2. A method according to claim 1, characterized in that the step of estimating a relationship is executed when the number of images obtained is greater than a predetermined threshold.

3. A method according to any one of claims 1 to 2, characterized in that the transmission step is performed when a correlation coefficient obtained during the regression is greater than a predetermined threshold.

4. A method according to any one of claims 1 to 3, characterized in that the step of estimating a relationship is followed by a step of calculating the distance of each point to the curve obtained and a step of estimating a new relationship carried out by excluding the points furthest from the curve obtained during the first estimation.

5. A method according to any one of claims 1 to 4, characterized in that it comprises, after estimating the first relation, the following steps: - obtaining a second plurality of subject images captured by terminals of the same terminal model, each image being associated with a focus value used during capture; - for each image of the second plurality of images, determination of a resolution of the subject in the image from the known size of the subject and the size in pixels of the subject in the image; - estimation of a second relationship between the focus value and the resolution of the subject in the image by regression on a set of points, each point corresponding to the pair focus value and resolution of the subject in the image for an image obtained belonging to the first or the second plurality of images; - transmission of the second estimated relationship to the terminals of the same model if the difference between the first relationship and the second relationship is greater than a predetermined threshold.

6. A method according to any one of claims 1 to 5, characterized in that the step of obtaining a first plurality of images comprises, for each image, a step of detecting a type of subject represented in the image.

7. A method according to any one of claims 1 to 6, characterized in that the subject belongs to one type of subject among the following types of subjects: - finger, - palm of the hand, - face, - document.

8. A method for optically recognizing a subject comprising, by a terminal including a camera, the following steps: - capturing an image of the subject associated with the focus value used during the capture; - optically recognizing the subject; characterized in that it comprises the following steps: - obtaining a relationship between the focus value and the resolution of the subject in the image; - determining a measured resolution of the subject in the image from a known size of the subject and the pixel size of the subject in the image; - determining an expected resolution of the subject in the image determined from the focus value and the relationship obtained; - rejecting the captured image on suspicion of fraud when the measured resolution and the expected resolution differ by a value greater than a predetermined threshold.

9. A method according to claim 8, characterized in that it comprises the following steps: - determining a resolution range of the subject in the image relative to the optical recognition of the subject; - determining the corresponding range of focus values ​​based on the relationship; - determining an indication to move the subject closer or further away during capture when the focus value is outside the range; and - displaying the indication during image capture.

10. Product computer program comprising instructions for carrying out the method according to any one of claims 1 to 9, when this program is executed by a processor (501).

11. Computer-readable non-transient recording medium on which is recorded a program for implementing the method according to any one of claims 1 to 9 when this program is executed by a processor (501).

12. A device for calibrating terminals of the same model equipped with a camera used in an optical recognition process, characterized in that it comprises a processor (501) configured to perform the following steps: - obtaining a first plurality of images of subjects captured by terminals of the same terminal model, each image being associated with a focus value used during capture; - for each image, determining a resolution of the subject in the image from the known size of the subject and the size in pixels of the subject in the image; - estimating a first relationship between the focus value and the resolution of the subject in the image by regression on a set of points, each point corresponding to the pair focus value and resolution of the subject in the image for a obtained image; - transmitting the first estimated relationship to the terminals of the same model.

13. Mobile terminal for non-contact optical recognition of a subject, the terminal comprising a camera and a processor (501) configured to perform the following steps: - capture of an image of the subject associated with focus value used during capture; - optical recognition of the subject; characterized in that it comprises the following steps: - obtaining a relationship between the focus value and the resolution of the subject in the image; - determination of a measured resolution of the subject in the image from a known size of the subject and the size in pixels of the subject in the image; - determination of an expected resolution of the subject in the image determined from the focus value and the relationship obtained; - rejection of the captured image when the measured resolution and the expected resolution differ by a value greater than a predetermined threshold.

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