229 results about "Iris image" patented technology

The embodiment of the invention provides an identity verification method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring multi-modal biological recognition data of a user in a current environment; performing feature extraction on the face image through a convolutional neural network to obtain face features; performing feature extraction on the iris image and the fingerprint information through a support vector machine to generate iris features and fingerprint features; identifying the face posture of the user in the current environment and the environment type of the current environment; adjusting weight values corresponding to the face features, the iris features and the fingerprint features according to the face postures and the environment types; and performing weighted fusion on the face feature, the iris feature and the fingerprint feature according to the weight value, and generating an identity verification result of the corresponding user in the current environment. According to the embodiment of the invention, the security and accuracy of user identity verification can be improved, and identity verification can be intelligently and accurately carried out on users in different complex environments in a financial scene.

The invention discloses a femtosecond laser refractive surgery cutting center positioning method and device, and relates to the field of refractive surgery cutting center localization, and the method comprises the steps: obtaining a lying position eye image and a sitting position eye image of a to-be-operated patient under set illumination brightness, and determining a first iris image and a first relative position; determining a second iris image according to the lying position eye image; according to the first iris image and the second iris image, a first eyeball rotation angle is determined, a second visual axis center and a second relative position of the patient to be operated can be further calculated, negative pressure suction is guided, and before negative pressure suction and after the center of a negative pressure suction ring is aligned with the second visual axis center, if movement occurs, the first eyeball rotation angle is determined; a third iris image in the negative pressure suction process is shot, then the center of a third visual axis is calculated, the center of the negative pressure suction ring is aligned to the center of the third visual axis, and the center of the third visual axis serves as the cutting center for operation. The cutting center can be accurately positioned.

The present application provides an iris recognition method and apparatus, an electronic device, and a storage medium. The method comprises: obtaining an iris image group acquired for an object to be recognized, wherein the iris image group comprises at least two iris images acquired at different acquisition angles for a same eye region of said object; performing stereo matching on the at least two iris images to obtain a disparity map between the at least two iris images, wherein the disparity map comprises disparity elements, and each disparity element represents a displacement amount in a specified direction between two pixel points corresponding to a same eye region element in the at least two iris images; in one iris image among the at least two iris images, determining a pupil edge on the basis of the displacement amounts; determining an iris region in the iris image on the basis of the pupil edge, and performing feature extraction on the iris region to obtain an iris feature; and performing identity recognition on said object on the basis of the iris feature.

The invention relates to the technical field of access control systems, and discloses a multi-mode biological recognition access control system, a biological recognition module is used for collecting biological characteristic images of a user, and the biological characteristic images at least comprise a fingerprint image, a face image and an iris image; the data processing module is used for performing data processing and analysis on the biological feature image acquired by the biological recognition module, extracting an effective feature value, and obtaining a target feature image; the identity determination module is used for performing multi-mode identification on the target feature image to obtain an identification result, and performing comparison to determine the identity of the user; the control module is used for controlling the opening or closing of the access control according to the comparison result of the identity determination module; the storage module is used for storing a biological characteristic template and access control parameter information of a user; the communication module is used for carrying out communication and linkage with a mobile terminal in a wired communication or wireless communication mode; the safety of the access control system is effectively improved, and the accuracy of identity verification is improved.

The invention relates to the technical field of identity authentication, in particular to a multi-lens iris recognition module and method, and the module comprises a lens adjustment unit, an image capture unit, an image quality evaluation unit, a color correction unit, a detail optimization unit and an identity authentication unit. According to the invention, the iris images with different depths of field are captured and the iris acquisition distance is enlarged by differentiating the aperture size and the focusing distance of the lens, so that the distance change of a user is effectively adapted, the recognition accuracy and the user experience are improved, and the high-quality iris images are screened through multi-parameter quality evaluation, so that the recognition precision is improved, and the user experience is improved. The method has the advantages that the image recognition accuracy is improved by enhancing the iris edge, the iris recognition efficiency and accuracy are optimized, the influence of environment change and individual difference on the recognition effect is reduced, and the recognition efficiency is improved. And a safer and more reliable identity verification scheme is provided for the user.

The invention discloses a digital currency transaction system based on an iris and a block chain, and the system comprises an iris registration module which is used for collecting an iris image, extracting an iris feature vector based on a deep hash network, and generating a unique identity label; the iris identity authentication module is used for extracting a current iris feature and calculating an identity hash value when a digital currency transaction request is obtained; the anti-counterfeiting iris detection module is used for detecting a texture abnormal condition of the current iris feature based on the anti-counterfeiting sample detection module, and confirming the validity of the iris when the detection is passed; and the transaction validity verification module is used for verifying the validity of the digital currency transaction based on zero-knowledge proof verification. Based on the iris features and the block chain technology, the defects of traditional digital currency in the aspects of identity authentication, account security and privacy protection are overcome.

The embodiment of the invention provides an anterior segment structure prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: encoding an anterior segment structure image and an ICL implantation direction before an ICL operation through an encoder module to obtain anterior segment structure image features; performing iris image generation on the anterior segment structure image features based on a first auxiliary decoder, a second auxiliary decoder and a geometric information sharing module to obtain an iris segmentation image output by the first auxiliary decoder and an iris contour and crystalline lens upper surface image output by the second auxiliary decoder; decoding the anterior segment structure image features based on a main decoder and a first auxiliary decoder to obtain an ICL postoperative anterior segment structure image; and displaying an ICL postoperative anterior segment structure image, an iris segmentation image and an iris contour and crystalline lens upper surface image in the ICL implantation direction. According to the embodiment of the invention, postoperative anterior segment structure prediction can be realized, and the accuracy is high.

The invention discloses an identity recognition method and device in a VR scene, equipment and a storage medium, and relates to the technical field of artificial intelligence, the method fuses inclination angle information as auxiliary information in an identity recognition process, inclination angle features can be used for representing feature weights of image areas in an original iris image, and the feature weights of the image areas in the original iris image can be used for representing the feature weights of the image areas in the original iris image. Therefore, different feature weights can be given to each image area during feature extraction, iris features can be better recognized, and the iris recognition accuracy is improved; meanwhile, an image generator model is trained by collecting low-quality iris image training samples in advance, and then super-resolution reconstruction is performed on the collected low-image-quality iris images by using the image generator model obtained by training to obtain high-resolution iris images, so that the problem that in the iris recognition process, the iris recognition efficiency is high is solved. And the identification effect is not good due to the problem of image quality.

The present invention discloses a dual identity authentication method for an automobile electronic anti-theft system, which relates to the field of automobile safety technology, and includes: a fingerprint acquisition module, a fingerprint recognition module, an iris acquisition module, an iris recognition module, a data storage module, a processing module, an automobile control module, and a time analysis module; the authentication steps are: S1, the data storage module pre-records the fingerprint and iris information of the owner, and stores them to provide data for subsequent comparison; S2, the fingerprint acquisition module acquires the fingerprint of the visitor; S3, the iris acquisition module acquires the iris image of the visitor. The present invention uses a dual identity authentication method, and in the vehicle electronic anti-theft system, the fingerprint and iris data can be uniformly stored in a secure database for centralized management. Compared with the traditional single information authentication method, it is more secure and anti-theft, protects the owner's vehicle, reduces the possibility of the owner's identity information being copied by criminals, and brings protection to the safety of the car.

The invention discloses an iris pupil real-time segmentation method based on deformable convolution and ellipse prior constraint, and relates to the technical field of computer vision and biological feature recognition. The method comprises the following steps: acquiring an original iris image, and preprocessing the original iris image by adopting dynamic shielding enhancement; constructing a lightweight encoder-decoder network, wherein the lightweight encoder-decoder network comprises an encoder, a decoder and a post-processing module; the encoder adopts a cascaded deformable convolution module and an elliptical attention mechanism module to perform feature extraction; training and optimizing the lightweight encoder-decoder network; carrying out multi-task loss calculation, introducing an elliptical parameter regression constraint design loss function on the basis of segmentation loss, and optimizing a segmentation result; and ellipse parameter refinement is carried out through a post-processing module, and ellipse fitting optimization is carried out on a segmentation result. Compared with the prior art, the iris pupil real-time segmentation method has the advantages that the precision is improved, the speed is optimized, and the robustness is high.

The invention relates to a target detection-based iris rapid and accurate segmentation method, which comprises the following steps of: obtaining an eye image for training, establishing a three-dimensional coordinate system and obtaining parameters of an iris ellipse by utilizing a three-dimensional mathematical relationship; constructing a directional bounding box fitted with the contour of the iris ellipse, the four edges of the directional bounding box being tangent to the iris ellipse, and solving the coordinates of the four vertexes of the directional bounding box; carrying out model training by taking the coordinates of the four vertexes of the directional bounding box and the parameters of the iris ellipse as model training labels so as to obtain a network model; the network model is used for detecting a directional bounding box surrounding an iris ellipse of the target eye image, and calculating and segmenting the iris ellipse according to the directional bounding box surrounding the iris. According to the method, a traditional iris segmentation mode depending on edge detection or pixel classification is abandoned, a brand new technical route based on deep learning target detection is adopted, and the limitation of a traditional method in processing a low-quality iris image is effectively overcome.

The invention discloses an iris boundary positioning method and device, a medium and equipment, and belongs to the field of biological feature recognition. The method comprises the following steps: acquiring an iris image to be positioned; inputting the iris image into a pre-trained deep learning model, segmenting the iris image by taking the iris inner boundary region and the iris outer boundary region as foreground targets and taking the rest parts as background regions, and segmenting iris inner boundary points and iris outer boundary points from the iris image; and respectively performing ellipse fitting according to the iris inner boundary point and the iris outer boundary point to obtain an iris inner boundary and an iris outer boundary. The method has good generalization and robustness for different changes, and the iris image positioning problem under the conditions of strabismus, squinting, low contrast and the like can be well solved. And through ellipse fitting, the iris inner boundary and the iris outer boundary are more regular, and subsequent boundary correction and iris region expansion are facilitated.

The invention discloses an access control system based on face recognition, relates to the technical field of biological recognition, and solves the problem that feature parameters are difficult to extract according to face images and iris images. Secondly, face comparison detection is difficult to carry out according to the extracted feature parameters of the face image; then, it is difficult to analyze the iris image of the incomer according to the face comparison result, and it is difficult to analyze the abnormal behavior according to the monitoring image; and finally, the risk coefficient is difficult to comprehensively analyze, and the risk level of the access control system is difficult to assess. By analyzing the face image and the iris image, the identity verification accuracy of the access control system is improved; abnormal behaviors of strangers in a monitoring image are analyzed by using a deep learning model, and a risk coefficient is comprehensively analyzed according to an identification failure rate and a potential abnormal coefficient, so that the risk level of the access control system is evaluated.

Included are: an iris image acquisition unit that acquires an iris image including an iris of a living body; a calculation unit that calculates a scale factor for the iris image, from a size of an iris area included in the iris image and from a desired size; a generation unit that generates a resolution-converted image in which resolution of the iris image is converted in accordance with the scale factor; and a post-transform feature vector extraction unit that extracts a post-transform feature vector that is a feature vector of the resolution-converted image.

The present invention relates to a multimodal recognition device for realizing health screening based on eye imaging and eye movement tracking, comprising a desktop end and a remote end, wherein the desktop end comprises: an image acquisition unit, which is arranged in the first screen display unit and acquires image information and eye movement information of the patient's eye along a first direction, wherein the image information comprises: an iris image, an eye surface image, an eye fundus image, and an eye position image; a first control unit, which recognizes the identity of the user in real time according to the iris image, and determines the health status of the user according to the eye surface image, the eye fundus image, the eye position image, and the eye movement tracking information. By acquiring the patient's iris image, real-time iris recognition and identity authentication are performed to ensure that the patient is always the same person, thereby improving the effectiveness of health screening. By acquiring the patient's eye surface image, the eye fundus image, the eye position image, and the eye movement data, the patient's health status is analyzed from multiple modalities to ensure the diversity and accuracy of health screening.

The invention discloses a drug taking detection method and device, a storage medium and equipment, and belongs to the field of biological feature recognition. The method comprises the following steps: firstly, acquiring at least one first image sequence containing a face image and at least one second image sequence containing an iris image; then acquiring micro-expression features according to the first image sequence, and acquiring iris texture features according to the second image sequence; and finally, performing drug taking judgment on the detected object according to the micro-expression features and the iris texture features. According to the invention, through fusion analysis of the face micro-expression features and the iris texture features of the detected object, whether the detected object takes drugs or not is comprehensively judged, and the accuracy of drug taking detection is improved. Moreover, non-contact drug taking detection is adopted, cooperation of a detected object is not needed, drug taking detection can be carried out under the condition that the detected object is non-inductive, and the method can adapt to different anti-drug detection environments.

The invention provides a biological multi-modal feature recognition method and device based on dynamic fuzzy reasoning. The method comprises the steps that an obtained face image, a fingerprint image and an iris image of a target object are preprocessed; performing feature extraction on the preprocessed face image, the fingerprint image and the iris image, and determining a three-dimensional Euclidean distance of the face, the fingerprint and the iris between the target object and an object to be verified in a database according to a feature extraction result; performing fuzzy reasoning based on the three-dimensional Euclidean distance of the face, the fingerprint and the iris to obtain a recognition score; comparing the identification score with a preset decision threshold value, when the identification score is greater than or equal to the decision threshold value, determining that the target object is matched with the to-be-verified object, otherwise, determining that the target object is not matched with the to-be-verified object; wherein the decision threshold value is obtained by training through a reward / punishment mechanism based on preset target recognition precision. The method can effectively cope with various environment changes, improves the recognition accuracy, and is suitable for application scenes needing extremely high safety.

The invention provides an iris acquisition identity verification method and system based on a smart watch, and the method comprises the steps: obtaining an iris image, and carrying out the missing judgment of the iris image, so as to mark a missing part and a real part of an iris; wherein the iris image is shot by an iris acquisition module on the smart watch; complementing the missing part based on a deep learning model to obtain a complemented image corresponding to the missing part, performing feature extraction on the complemented image to obtain a simulation feature, performing feature extraction on the real part of the iris image to obtain a real feature, and processing the simulation feature and the real feature based on a weighted average algorithm to obtain a comprehensive feature vector; and performing identity verification through the comprehensive feature vector, and outputting an identity verification result. According to the method for identity verification through combination of the real features and the simulation features, the verification precision under the condition that the iris image is partially missing can be improved, meanwhile, the number of times of repeated shooting of the smart watch is reduced, and the verification efficiency is improved.

The invention discloses an iris image quality evaluation method and system, and the method comprises the following specific steps: 1, image preprocessing: improving the image quality, and highlighting the iris features; 2, feature region segmentation: detecting iris and pupil boundaries by adopting a Hough circle transformation or ellipse fitting technology, and detecting eyelid and eyelash boundaries by adopting Canny edge detection gradient intensity; 3, multi-dimensional quality evaluation: comprehensively quantifying the image quality through four indexes of available iris region calculation, contrast analysis, geometric characteristic evaluation and image quality evaluation; 4, adjustable weight scoring: generating a comprehensive quality score through a comprehensive quality scoring formula in combination with the adjustable weight of the user; and 5, interactive manual adjustment: the user can manually adjust the boundary of the iris and the pupil, and the evaluation result is updated in real time. The method is suitable for the fields of iris recognition, eye movement tracking, human-computer interaction, medical diagnosis and the like, and has remarkable practicability and flexibility.

A server for predicting a disease through AI-based iris analysis and a method for operating same are disclosed. The server for predicting a disease through AI-based iris analysis comprises: an image preprocessing unit that performs preprocessing on an iris image received from a user terminal; a prediction model training unit that trains an iris-based disease prediction model in a supervised manner using pre-collected learning data; a prediction model tuning unit that performs additional learning on the supervised iris-based disease prediction model; and a disease prediction unit that determines a predicted disease corresponding to the iris image using candidate diseases obtained from the additionally trained iris-based disease prediction model and transmits disease prediction information including the determined predicted disease to the user terminal.

The present invention provides a technology that acquires a high resolution iris image more quickly than before. An iris capture apparatus according to one example embodiment of the present invention includes a rotatable movable mirror; a control unit that controls rotation of the movable mirror; a capture unit that captures different regions of a face of a user via the movable mirror and outputs a group of images every time the control unit rotates the movable mirror by a predetermined angle; and an iris image acquisition unit that acquires an image of an iris of the user from the group of images.

The present invention discloses a three-dimensional iris reconstruction and expansion method, which relates to the field of intelligent medical care. The method comprises the following contents: step one, generating a three-dimensional iris tag; step two, establishing an iris depth map prediction model; and step three, expanding the iris. The present invention utilizes the anatomical constraint relationship between multiple iris images in a video stream to accurately predict iris depth information, realize three-dimensional iris reconstruction, and expand the iris into a two-dimensional rectangular image and depth map. The method overcomes the defect of insufficient depth of a traditional single image and utilizes multiple pictures in a video to predict iris image depth information. The iris textures between the iris pictures have an anatomical constraint relationship, which can effectively predict iris image depth information, improve the robustness of iris recognition, and is suitable for a variety of application scenarios.

To provide an authentication device and an authentication system capable of authenticating an object using an iris image.MEANS: An authentication device includes first imaging means having an imaging element and acquiring an iris image by imaging the iris of a subject, and a display displaying information regarding authentication using the iris image on a display surface, the imaging element is positioned on the rear side of the display and overlapping with the display surface when viewed from a direction perpendicular to the display surface, and the optical axis of the first imaging means intersects with the perpendicular direction.SELECTED DRAWING: Figure 7

The application discloses an iris recognition method and device, system and storage medium, and performs preprocessing such as polar coordinate unfolding on an input original iris image in sequence; multi-level image features are extracted through an inverse residual module; subsequently, a global depth convolution GDConv operator is introduced to replace a global average pooling layer, so that spatial structure distribution features of iris textures are retained under the premise of extremely low calculation complexity; finally, an ArcFace loss function is used to optimize the distribution of features in a hyperspherical space, and the class separability of the features is enhanced by introducing an additive angle interval. By using the technical scheme of the application, the technical problems that spatial topological information is lost due to a global average pooling GAP operation in iris feature extraction by using an existing lightweight convolutional neural network, and the feature discrimination is reduced due to network lightweight are solved.

A computing system receives a code that is generated based upon an image of an iris of a first user. The image of the iris of the first user is captured by way of an iris scanning device that is under control of a second user. The computing system executes a search over a plurality of codes stored in a data store based upon the code, where the plurality of codes are generated based upon images of irises of a plurality of users. The computing system determines whether the code is included in the plurality of codes based upon search results for the search. Based upon a determination that the code is not included in the plurality of codes, the computing system causes an amount of cryptocurrency to be transferred to a cryptocurrency wallet of the second user.

A ophthalmic laser-assisted corneal lenticule extraction procedure that uses wavefront measurements to guide the formation of the corneal lenticule. The wavefront map measured from a free eye using a wavefront aberrometer is registered to the cornea of a docked eye based on comparisons of iris images and corneal markings. The docked-eye cornea-registered wavefront map is then corrected to be consistent with the Munnerlyn formula for the spherical power, and adjusted for any physician adjustments and / or myopia error due to a flat add in the lenticule, using Zernike polynomials. The corrected and adjusted wavefront map is then used to calculate the profiles of the bottom and top lenticule incisions in the applanated cornea, where higher-order components in the wavefront map are distributed to the bottom lenticule incision alone and lower-order components in the wavefront map are distributed to both the bottom and the top lenticule incision.

The application relates to the field of image processing, in particular to an iris image matching method and device, equipment and medium, which are used to solve the problem of large calculation error in the iris image matching process. The method determines a plurality of target Mahalanobis distances based on each feature point of a to-be-detected iris image. It should be noted that each target Mahalanobis distance is determined based on the gradient parameters corresponding to any two target feature points of the to-be-detected iris image. Based on the sum of the plurality of target Mahalanobis distances and the sum of a plurality of standard Mahalanobis distances, the matching degree between the to-be-detected iris image and a pre-stored standard iris image is determined, wherein the standard Mahalanobis distance is determined based on the standard iris image. The above-mentioned iris image determination method based on Mahalanobis distance considers the internal relationship between the feature points, improves the matching accuracy between the to-be-detected iris image and the pre-stored standard iris image, and improves the reliability of the safe box in use.

In the Iris based biometric recognition systems, iris needs to be segmented before comparison and hence iris segmentation is a crucial process in iris based biometric recognition systems. Existing techniques are unable to segment iris due to occlusions and require complex and time consuming algorithms to remove these occlusions. Other deep learning based solutions require precise annotations of the iris regions along with eyelids and eyelashes. The present disclosure initially generates polygons on the iris and pupil area using a pre-trained detection model. Internally covered vertices and externally exposed vertices of the generated polygons are computed to construct curvature of the iris region. A pupil region is generated by filtering the plurality of vertices lying inside pupil circle region from the plurality of externally exposed vertices. Further, a segmented iris image is generated by removing the pupil region from the input image based on the curvature of the iris region.

Disclosed herein are methods, apparatus, and systems for iris recognition. A method includes acquiring at least two angularly differentiated iris images from a subject needing access, processing each of the at least two angularly differentiated iris images to generate at least one boundary delineated image from one of the at least two angularly differentiated iris images, applying image comparative analysis to the at least two angularly differentiated iris images to generate a boundary delineated image when the processing fails to produce the at least one boundary delineated image, segmenting and encoding one of the at least one boundary delineated image or the boundary delineated image to generate at least one iris template, matching the at least one iris template against an enrolled iris, and accepting the subject for access processing when the at least one iris template matches the enrolled iris.

The invention relates to a detection device and a detection method, and the device comprises a multiband light source module which is used for emitting light of a target band according to a light source control instruction to irradiate a target iris; the quantum dot spectrum sensing module is used for collecting an iris image and an iris spectrum image corresponding to a target iris, the quantum dot spectrum sensing module comprises a quantum dot spectrum sensor, the quantum dot spectrum sensor comprises a quantum dot light filter and a light sensing element, the quantum dot light filter is located on the receiving side of the light sensing element, and the light sensing element is located on the receiving side of the light sensing element; the iris spectral image comprises spectral information of different wavebands corresponding to each part in the target iris; and the data processing module is used for determining iris feature information corresponding to the target iris according to the iris image and the iris spectrum image. According to the embodiment of the invention, the detection device can improve the collection efficiency and image quality of the iris image and the iris spectrum image, improves the accuracy and reliability of the iris feature information corresponding to the subsequently obtained target iris, and reduces the cost of the device.