396 results about "Vein recognition" patented technology

The invention provides a fingerprint and finger vein bimodal recognition decision level fusion method. Two modules, namely a fingerprint module and a vein module are included. The fingerprint module and the vein module read fingerprint images and vein images; the read fingerprint images and vein images are subjected to image quality evaluation according to respective image characteristics to acquire quality scores; the fingerprint images and vein images are preprocessed and recognized, wherein the fingerprint recognition adopts a minutiae-based matching method and the vein recognition adopts an improved Hausdorff distance mode, and respective recognition results are obtained; and finally, a weight is designed according to image quality scores of the two modes, and the recognition results are subjected to decision level fusion according to the weight and a final recognition result is obtained. Based on the fact that the performance of the system after fusion is superior to that of a single fingerprint recognition or finger vein recognition system, the method has strong practicability.

The invention discloses a finger vein recognition and safety authentication method, a terminal and a system. The finger vein recognition and safety authentication method comprises a collection step, that is, information of a vein image of the finger part is collected, and encryption is performed on the vein image; an image processing step; a feature extraction step, that is, detail point feature extraction and storage are performed on the vein image conforming to quality evaluation requirements; and feature point matching step, that is, stored vein image detail point features are taken, comparative analysis is performed on the stored vein image detail point features and a vein image to be detected, detail point position and angle information is compared, a matching operation is completed,and a result is outputted. Meanwhile, the invention further comprises a finger vein recognition and safety authentication terminal and a system. According to the invention, a series of image processing and feature extracting operations are performed on the collected finger vein image, so that the finally recognized vein image is enabled to be accurate and clear, and the accuracy and the response speed of the whole finger vein recognition are effectively enhanced. Meanwhile, encryption processing is performed on the vein image, so that the confidentiality of the whole recognition system is improved, and the system is enabled to be safer and more reliable.

The invention discloses a finger vein recognition method fusing local features and global features. At present, a number of vein recognition methods adopt the local features of a vein image, so that the recognition precision of the vein recognition methods is greatly affected by the quality of the image; the phenomena of rejection and false recognition are liable to appear. The finger vein recognition method provided by the invention comprises the following steps: firstly, performing pretreatment operations such as finger area extraction of a read-in finger vein image, binarization and the like; then, according to the point set of extracted detail features, realizing the matching of the local features within a certain angle and a certain radius by virtue of a flexible matching-based local feature recognition module; using a global feature recognition module for vein image recognition to realize the matching of the global features as the global feature recognition module is used for analyzing bidirectional two-dimensional principal components and can better display a two-dimensional image data set on the whole; finally, designing weights according to the correct recognition rates of the two recognition methods, performing decision-level fusion to the results of two classifiers, and taking the fused result as a final recognition result. The method is applied to finger vein recognition.

The invention provides a secondary classification fusion identification method for fingerprint and finger vein bimodal identification. A fingerprint module and a vein module are used as primary classifiers, and a secondary decision module is used as a secondary classifier. The method comprises the following steps of: reading a fingerprint image and a vein image through the fingerprint module and the vein module; pre-processing the read images respectively and extracting characteristic point sets of the both; performing identification on the images respectively to obtain respective identification results, wherein the fingerprint identification adopts a detail point match-based method, and the vein identification uses an improved Hausdorff distance mode to perform identification; forming a new characteristic vector by using the extracted fingerprint and vein characteristic point sets in a characteristic series mode through the secondary decision module so as to form the secondary classifier and obtain an identification result; and finally, performing decision-level fusion on the three identification results. The method has the advantages of making full use of identification information of fingerprints and finger veins, and effectively improving the accuracy of an identification system, along with high identification rate.

The invention provides an intelligent entrance guard system based on fingerprint and finger vein joint recognition. The entrance guard system comprises a power supply management circuit, an electric control lock, an image collection device and an information processing control section, wherein the information processing control section is used for obtaining data and information of images collected in the image collection device, carrying out image algorithm processing on the data of the images and carrying out authentication on a fingerprint image and a finger vein image; the power supply management circuit is electrically connected with the image collection device, the information processing control section and the electric control lock respectively; and the image collection device and the electric control lock are respectively connected with the information processing control section via a signal. According to the intelligent entrance guard system, the accuracy rate and the uniqueness of entrance guard system authentication are ensured by bimodal biological characteristics authentication combining a fingerprint identification technology and a finger vein identification technology, thereby improving the safety, the reliability and the anti-counterfeit property of the entrance guard system, and meanwhile, the entrance guard system disclosed by the invention has multifunctionality, thereby achieving intelligentization of the entrance guard system and improving the user experience.

The invention discloses a finger vein recognition device and method with the living body finger detection function. The device comprises a near-infrared light lamp bank arranged around a finger and further comprises a guide groove located in the middle of the device and guiding the placement position of the finger. A finger placement platform is arranged at the bottom of the guide groove. A camera device for photographing veins is arranged on the lower portion of the platform. A camera window is arranged in the center of the finger placement platform. An optical filter is arranged between a camera of the camera device and the camera window. An infrared pulse wave detection sensor is arranged on or below the finger placement platform. A control module is further arranged in the device, wherein the control module is used for controlling the photographing process, processing photographed data and processing data detected by the infrared pulse wave detection sensor. According to the device and method, pulse waves are detected through the infrared pulse wave detection sensor arranged in the device, then, whether a test object has a living body finger or not is judged, and thus the reliability of finger vein recognition is improved.

The present invention discloses a finger vein recognition system comprising an image catching module, an image preprocess module, a feature points calculating module, a feature database, a first comparing module and a second comparing module. The feature points calculating module is connected to the image preprocess module for calculating a set of distances among a plurality of feature points. The first comparing module is connected to the feature points calculating module and the feature database for comparing the set of distances and generating a compared result of feature points. The second comparing module is connected to the image preprocess module, the feature database and the first comparing module for catching a set of vein shape and comparing the set of vein shape with the feature database for generating a compared result of shape similarity. Wherein, the second comparing module combines the compared result of feature points and the compared result of shape similarity for generating a recognized result of the finger vein.

The invention discloses an identity recognition method and a system based on the finger vein characteristics. The method comprises the following steps of preprocessing a read-in finger vein image to obtain a corrected image sample unit; extracting characteristic values of the image sample unit, wherein the characteristic values comprise quality grade, maximum curvature characteristics and mean curvature characteristics, and the quality grade is divided into an excellent grade, an average grade and a poor grade; carrying out matching degree calculation on the characteristic values of the image sample unit and characteristic values of a registered image unit prestored in a database according to a matching strategy so as to carry out identity recognition, wherein the quality grade, the maximum curvature characteristics and the mean curvature characteristics corresponding to the registered image unit are prestored in the database, and the matching strategy is characterized in that the weight and the priority of the matching degree of the maximum curvature characteristics and the mean curvature characteristics are set according to the quality grades of the image sample unit and the registered image unit. According to the identity recognition method and the system based on the finger vein characteristics, the accuracy and the robustness of finger vein recognition are improved, and the finger vein recognition technology can be favorably popularized and applied.

The present invention discloses a convolutional neural network-based embedded finger vein identification method enabling a counterfeit detection capability. The method includes the following steps that: S1, a plurality of finger vein images of a plurality of levels of light intensities are acquired, a finger vein image with the highest resolution is selected, the ROI (region of interest) of the captured image is preprocessed; S2, a local binary pattern (LBP) is utilized to perform texture feature coding on the high frequency information of the finger vein image; S3, the high frequency part features of the finger vein image are extracted through using a high-pass filter, finger image features are extracted through a vein identification shallow convolutional neural network; and S4, an SVM (support vector machine) classifier is utilized to perform counterfeit detection so as to distinguish the authenticity of the vein image. With the method adopted, the problem that a printed counterfeited vein image can fool an existing identification system can be solved, and the security of an actual vein identification system can be improved; the problem of the decrease of the identification accuracy of the actual system which is caused by factors such as low vein image quality and finger axial deflection can be solved.

The invention discloses an automobile anti-theft system based on human face identification technology multi-identity authentication. The automobile anti-theft system consists of an engine control module, a human face identification module, a fingerprint acquisition and identification module, a digital vein identification module, a body temperature induction module, a data storage module, a communication module and a control module. On the basis of not covering the original functions of an automobile or damaging an original automobile circuit, an ignition device, an oil circuit and an engine of the automobile are controlled. If authentication fails, the automobile can be prevented from being stolen due to failure in launching, thereby greatly improving the theft resistance.

A specific item within an item class is identified by defining sets of descriptor data from a training library. The collected descriptor data is grouped and organized into a hierarchical tree, where each leaf node is defined by relations between corresponding parts of the descriptor data. Registrable sets of descriptor data are then identified from a collection of registrable samples. The registrable sets of descriptors are sorted into the hierarchical tree. When an input sample to be identified is received, a test set of descriptor data is generated from the input sample. The test set is then sorted into the hierarchical tree. Each leaf node that receives a part of the test set provides a vote for the registered samples it contains. The registered sample with the most votes is deemed a match for the input sample.

The invention discloses a finger vein recognition method by interested areas and directional elements. Existing vein recognition algorithms can be divided into two types, namely local feature utilization and global feature utilization, but the problems of long time consumption, large computing capacity and low image quality cannot be solved simultaneously. The method includes firstly reading a finger vein image, extracting an interested area by the interested area extraction method based on rotation correction, then constructing vectors by adopting gradient magnitude and direction of pixel points on finger vein lines to represent direction feature of the vein lines in the interested area, combining the direction feature and membership degree of image sub-blocks, creating feature vectors of the vein image, finally during the finger vein matching process, measuring similarity of different linear feature vectors by adopting cross-correlation coefficient, and figuring out matching results. The method is used for finger vein recognition.

The invention discloses a finger vein feature extraction method. According to the method, the stability of vein feature extraction can be improved, more features can be obtained, and therefore the vein recognition accuracy can be improved conveniently. The method includes the steps that under irradiation of an infrared light source, an infrared camera is used for acquiring finger vein images; the finger vein images with brightness meeting requirements are obtained by adjusting parameters of the infrared light source and parameters of the infrared camera; a plurality of images to be processed are acquired by the adoption of the determined parameters of the light source and the determined parameters of the camera and converted into binary images of vein lines; vein existing points are sought for by combining the binary images of the vein lines, and a synthetic vein image is formed; finally, image features of finger veins are extracted from the synthetic vein image.

The invention relates to a finger vein and fingerprint fusion identification method of a middle part of a finger. A near-infrared transmission image of a middle part of a finger is collected; a binary fingerprint image is extracted based on a local gradient feature analysis and binary finger vein information is extracted by using a local-OTSU-based segmentation method; according to an SIFT-based matching algorithm, a similarity between a sample binary fingerprint image and a template binary fingerprint image is calculated, and for the determined similar binary fingerprint images, an affine transformation coefficient between the images is calculated; and normalization is carried out on the sample binary fingerprint image by using the affine transformation coefficient, a similarity between the sample binary finger vein normalization image and a template binary finger vein image is measured by using a template matching method, and whether the current sample image is consistent with the template image is determined. Therefore, defects of poor security of single fingerprint identification and poor usability of single finger vein identification can be overcome. The method can be widely applied to a high-security-level identity identification system.

The invention discloses a small data set finger vein recognition method based on a neural network. The method comprises the following steps: constructing and preprocessing a data set; carrying out data expansion on all types of images by utilizing a data enhancement algorithm; utilizing a convolutional neural network to construct a model suitable for data; training the designed model; acquiring atarget image by using acquisition equipment for preprocessing, and outputting a finger vein feature vector of the preprocessed image through model output; and comparing with a stored finger vein feature vector template to determine the category of the input finger vein. According to the method, through combination of a neural network and data enhancement, features with robustness are extracted; and through Euclidean distance comparison between different image output feature vectors, different types of finger vein images are distinguished according to the size of the distance, so that finger vein recognition can obtain high accuracy on the premise of a small data set.

The invention discloses a finger vein recognition method based on an FCN full convolution network. The finger vein recognition method comprises the steps that collecting a finger vein image; preprocessing the finger vein image to obtain a preprocessed finger vein image; performing edge detection, least square linear fitting and direction correction on the preprocessed finger vein image, and extracting a median section ROI finger vein image with rickest finger veins on the basis of the edge detection, the least square linear fitting and the direction correction; extracting features from the extracted ROI finger vein image by adopting the trained FCN full convolution network, and classifying each pixel point to obtain a to-be-registered/recognized finger vein image with distinguished vein points and background points; and collecting a to-be-registered finger vein image, constructing a registered finger vein database, and carrying out retrieval and recognition on the to-be-recognized finger vein image in the finger vein database to obtain a matching recognition result. In the embodiment of the invention, a clearer finger vein image can be obtained, and the matching recognition efficiency is higher and more accurate.

The invention discloses a finger vein recognition method based on a convolutional neural network and supervised discrete hashing. The finger vein recognition method includes the steps: collecting finger vein images through bilateral infrared irradiation, and carrying out preprocessing, edge detection, direction correction and ROI extraction; extracting features of the ROI finger vein image by adopting a Res2net convolutional neural network; carrying out binary coding by adopting a discrete hash algorithm model; taking the extracted binary coding features as finger vein features to be registered/identified; and constructing a finger vein image database, carrying out the above processing of the to-be-recognized finger vein image, obtaining the recognized feature codes, carrying out the retrieval and recognition of the feature codes in the finger vein database one by one, obtaining the similarity through the Hamming distance measurement, and obtaining a matching recognition result. In theembodiment of the invention, feature codes with higher representation capability can be obtained through deep learning, and the discrete hash algorithm can enable the feature template to be smaller in size, so that the finger vein recognition method is more efficient in massive matching face matching.

The invention relates to a mobile terminal identity authentication unlocking method which comprises the following four steps of: a step 1 of carrying out vein characteristic registration, i.e. acquiring vein characteristics of a user by a camera of a mobile phone and carrying out vein characteristic registration; a step 2 of configuring information of target equipment which needs to be unlocked, such as a MAC (Media Access Control) address; a step 3 of carrying out vein characteristic authentication, i.e. acquiring the vein characteristics of the user by the camera of the mobile phone and carrying out comparison authentication when the user requests to unlock the equipment; and a step 4 of unlocking the target equipment, i.e. selecting the target information configured to the target equipment which needs to be unlocked to unlock the target equipment by the user. According to the invention, any additional equipment does not need to be arranged on the existing mobile phone, the high-safety vein identification and identity authentication can be implemented only by installing corresponding software, and a native machine or other external equipment is unlocked after the user identity authentication is passed; and the mobile terminal identity authentication unlocking method has the advantages of low cost, high safety, convenience for the user, easiness for popularization and the like.

The invention relates to a finger vein identification method, an apparatus and a vein authentication terminal device. The method includes controlling an image acquisition apparatus to rotate about a sample finger once the sampled finger entered in an identification area is detected, driving the image acquisition apparatus to sample the vein of the sampled finger to obtain a plurality of sampling images, comparing and matching the sampling images with preset sample images, and determining passing of vein identification if at least one sampling image matches with the sample images. The vein false rejection rate can be maintained even when the finger plane is not perpendicular to the image acquisition apparatus. The invention is easy to realize.

The invention discloses a deep learning palm vein recognition system based on cloud side cooperative computing. The system is mainly composed of a cloud computing layer, an edge computing layer and aterminal layer. The calculation and storage capabilities of the whole identification system are determined through the combination of three layers; the system specifically comprises a cloud storage module, palm vein acquisition and identification equipment and a client; the cloud storage module can provide computing resources required by training for the model and transmits the trained model to anedge calculation module of the palm vein acquisition equipment; the terminal collects the recognition condition of the deep convolutional neural network model, and feeds back the recognition condition to the deep convolutional neural network model chip on the cloud storage module through the edge calculation module.Continuous training, application, feedback and retraining are performed, and the accuracy of the finally obtained palm vein recognition model is greatly improved through repeated effective iterative training. The rear-end cloud storage module can also realize rapid deployment and unified management of the palm vein recognition system according to the resource configuration and management capability of the cloud platform.

The invention provides a device for acquiring biological recognition information. The device comprises a detection portion for outputting a result inclusive of face confirmation information when detecting the face of a person makes an appearance in a detection range; a first image acquisition portion for acquiring a face image appearing in the detection range after receiving the face confirmation information; and a second image acquisition portion for acquiring a palm image appearing in the detection range, wherein the first image acquisition portion and the second image acquisition portion acquire respective images at different time. The device may simultaneously acquire face and palm biological information, has a high face recognition passing rate and high face recognition accuracy, and may achieve a vein recognition and anti-counterfeiting capability of the palm image. Further, since the face image and the palm image are both acquired and compared in a non-contact way, the device achieves a clear and convenient image acquisition process and good user experience.

The invention discloses a finger vein recognition method based on individualized weight, and the method can be used for effectively overcoming negative influence of noise levels on recognition accuracy and improving the recognition performance and robustness of a finger vein recognition system. The method comprises a training process and a recognizing process, wherein the training process comprises the following steps of: firstly preprocessing training images; then respectively extracting LBP (local binary pattern) features of the training images; and finally obtaining a weight bitmap W through training. The recognizing process comprises the following steps of: preprocessing a tested sample image firstly; then carrying out LBP feature extraction; and finally calculating the hamming distance between the tested sample image and a database template, and determining a recognition result according to a set threshold, wherein if figures on two sides of an XOR operator of which the symbol isshown in the specification are identical, the result is '0', otherwise, the result is '1', and if DA is less than the set threshold theta, the tested image belongs to class A and theta is 0.15.

The invention discloses a two-factor authentication system based on fingerprint and vein recognition. The two-factor authentication system comprises a vein acquisition unit and a fingerprint acquisition unit which are mounted in a same apparatus, wherein a vein module and an external communication interface connected with each other are arranged in the vein acquisition unit; a fingerprint module, a control/network transmission module and an external communication interface are arranged in the fingerprint acquisition unit and are connected with one other; and the vein module is connected with the fingerprint module. A vein algorithm processing unit is used for primarily authenticating identity of a user; then a fingerprint of the user is acquired; and a fingerprint algorithm processing unit is used for finally determining whether the user has authority. Through vein and fingerprint authentication, the two-factor authentication system provided by the invention can effectively prohibit counterfeiting fingerprints, and can widely expand the application space of fingerprint and vein recognition.

The invention discloses a finger vein recognition method and system based on triple loss and a lightweight network. The method comprises the following steps: preprocessing a finger vein sample image in a database; constructing a small lightweight network model structure, selecting a triple (A, P, N) in the training set, and carrying out network training; taking the training set as character information of a to-be-input finger vein database, reading images in the training set and inputting the images into the model to obtain an embedins vector library; inputting a certain sample image into the network model, and if the similarity between the sample image and the sample image in the training set is greater than a threshold value, determining that the person is not in the database; otherwise, selecting a distance minimum value in a range conforming to a threshold value, and identifying the distance minimum value as a person with the highest similarity. According to the method, triple loss is introduced to be combined with a lightweight network and a training model, spatial mapping of vein features is achieved, the situation that a network model needs to be trained multiple times when a database dynamically changes is avoided, and finally the precision and efficiency of finger vein recognition are effectively improved.