CN111880848A - Switching method and device of operating system, terminal and readable storage medium - Google Patents
Switching method and device of operating system, terminal and readable storage medium Download PDFInfo
- Publication number
- CN111880848A CN111880848A CN202010679897.0A CN202010679897A CN111880848A CN 111880848 A CN111880848 A CN 111880848A CN 202010679897 A CN202010679897 A CN 202010679897A CN 111880848 A CN111880848 A CN 111880848A
- Authority
- CN
- China
- Prior art keywords
- fingerprint
- terminal
- features
- information
- operating system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 67
- 238000003825 pressing Methods 0.000 claims description 33
- 238000004590 computer program Methods 0.000 claims description 15
- 238000012795 verification Methods 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 8
- 230000001815 facial effect Effects 0.000 claims description 3
- 210000000554 iris Anatomy 0.000 description 49
- 238000004422 calculation algorithm Methods 0.000 description 15
- 238000012545 processing Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 8
- 238000001514 detection method Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 7
- 210000000887 face Anatomy 0.000 description 7
- 230000009466 transformation Effects 0.000 description 7
- 239000013598 vector Substances 0.000 description 7
- 238000000605 extraction Methods 0.000 description 6
- 210000001508 eye Anatomy 0.000 description 6
- 238000007781 pre-processing Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 238000012549 training Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 238000010606 normalization Methods 0.000 description 4
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 239000013078 crystal Substances 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 238000003909 pattern recognition Methods 0.000 description 3
- 210000001747 pupil Anatomy 0.000 description 3
- 210000003786 sclera Anatomy 0.000 description 3
- 229910052710 silicon Inorganic materials 0.000 description 3
- 239000010703 silicon Substances 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 239000003792 electrolyte Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 239000011241 protective layer Substances 0.000 description 2
- 229910052594 sapphire Inorganic materials 0.000 description 2
- 239000010980 sapphire Substances 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000007920 subcutaneous administration Methods 0.000 description 2
- 206010040925 Skin striae Diseases 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 210000005069 ears Anatomy 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 210000002615 epidermis Anatomy 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 210000000744 eyelid Anatomy 0.000 description 1
- 230000008175 fetal development Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 239000010410 layer Substances 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012567 pattern recognition method Methods 0.000 description 1
- NGVDGCNFYWLIFO-UHFFFAOYSA-N pyridoxal 5'-phosphate Chemical compound CC1=NC=C(COP(O)(O)=O)C(C=O)=C1O NGVDGCNFYWLIFO-UHFFFAOYSA-N 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 210000001525 retina Anatomy 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/4401—Bootstrapping
- G06F9/4406—Loading of operating system
- G06F9/441—Multiboot arrangements, i.e. selecting an operating system to be loaded
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Collating Specific Patterns (AREA)
Abstract
The invention relates to the technical field of operating system switching, and discloses a switching method, a switching device, a switching terminal and a readable storage medium of an operating system. Wherein, the method comprises the following steps: acquiring fingerprint information of a user; analyzing according to the fingerprint information to obtain fingerprint features to be matched of the fingerprint information; judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal; if so, acquiring the identity identification information of the user; analyzing according to the identity recognition information to obtain identity recognition features to be matched of the identity recognition information; judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal; if so, switching an operating system of the terminal; the switching method of the operating system has strong switching mode confidentiality, does not expose the terminal to have double systems, and meets the use requirements of users.
Description
Technical Field
The present invention relates to the field of operating system switching technologies, and in particular, to a method, an apparatus, a terminal, and a readable storage medium for switching an operating system.
Background
With the rapid development of the internet, the use environment of the user is also changing continuously, and in order to ensure that important data and privacy files in the user terminal cannot be revealed, the use of the dual-system terminal is favored by people. The user can save important confidential files in the security system in the dual system, and conditions are provided for ensuring the privacy security of the terminal.
In the prior art, the switching mode of the dual-system terminal is usually completed by depending on a system switching icon in the terminal, and the method can expose the terminal as the dual-system terminal when a user switches the system, so that the confidentiality is poor and certain potential safety hazards exist.
Disclosure of Invention
The invention aims to provide a switching method, a switching device, a switching terminal and a readable storage medium of an operating system, and aims to solve the problem that in the prior art, when a dual-system terminal relies on a system switching icon in the terminal to complete system switching, the dual-system terminal is exposed to be the dual-system terminal, and the confidentiality is poor.
In a first aspect, an embodiment of the present invention provides a method for switching an operating system, which is applied to a terminal, where the terminal at least has a first operating system and a second operating system that can be switched back and forth, and the method for switching includes:
acquiring fingerprint information of a user;
analyzing according to the fingerprint information to obtain the fingerprint characteristics to be matched of the fingerprint information;
judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;
if so, acquiring the identity identification information of the user;
analyzing according to the identity recognition information to obtain identity recognition features to be matched of the identity recognition information;
judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal;
and if so, switching the operating system of the terminal.
In some embodiments, the handover method further comprises:
acquiring the pressing duration of the fingerprint sensing element of the terminal continuously pressed by the fingerprint of the user;
judging whether the pressing time length exceeds a preset threshold value or not;
and if so, acquiring the fingerprint information of the user.
In some embodiments, the first operating system is a normal mode system; the second operating system is a secure mode system for encrypting important data.
In some embodiments, the identification information includes one or more of voiceprint information, facial information, iris information, and body impedance information.
In some embodiments, the handover method further comprises:
and if the fingerprint features to be matched are not the standard fingerprint features in the fingerprint feature database prestored in the terminal, quitting the verification, and maintaining the original system by the terminal.
In some embodiments, the handover method further comprises:
and if the identification features to be matched are not the standard identification features in the identification feature database prestored in the terminal, quitting the verification, and maintaining the original system by the terminal.
In a second aspect, an embodiment of the present application provides a switching device for an operating system, which is applied to a terminal, where the terminal has at least a first operating system and a second operating system that can be switched back and forth, and the switching device includes:
the first acquisition unit is used for acquiring fingerprint information of a user;
the first analysis unit is used for analyzing the fingerprint information to obtain the fingerprint features to be matched of the fingerprint information;
the first judgment unit is used for judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;
the second acquisition unit is used for acquiring the identity identification information of the user when the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;
the second analysis unit is used for analyzing the identity recognition information to obtain the identity recognition features to be matched of the identity recognition information;
the second judging unit is used for judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal;
and the switching unit is used for switching the operating system of the terminal when the second judging unit judges that the identification features are standard identification features in an identification feature database prestored in the terminal.
In some embodiments, the apparatus further comprises:
the pressing duration acquisition unit is used for acquiring the pressing duration of the fingerprint sensing element of the terminal continuously pressed by the fingerprint of the user;
the pressing duration judging unit is used for judging whether the pressing duration exceeds a preset threshold value, and if the pressing duration judging unit judges that the pressing duration exceeds the preset threshold value, the first acquiring unit acquires the fingerprint information of the user.
In a third aspect, a terminal provided in an embodiment of the present application includes a memory and a processor;
the memory stores a computer program;
the processor is configured to execute the computer program and implement the above-mentioned switching method of the operating system when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a readable storage medium, where a computer program is stored, and the computer program, when executed by a processor, causes the processor to implement the above-mentioned switching method for an operating system.
Compared with the prior art, the invention mainly has the following beneficial effects:
the provided switching method of the operating system acquires the fingerprint information of the user; analyzing according to the fingerprint information to obtain fingerprint features to be matched of the fingerprint information; judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal; if so, acquiring the identity identification information of the user; analyzing according to the identity recognition information to obtain identity recognition features to be matched of the identity recognition information; judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal; and if so, switching the operating system of the terminal. The method comprises the steps that a fingerprint characteristic database and an identity recognition characteristic database are preset in a terminal, when acquired fingerprint information is analyzed to be fingerprint characteristics and can be matched with standard fingerprint characteristics in the fingerprint characteristic database, identity recognition information is acquired, when acquired identity recognition information is analyzed to be identity recognition characteristics and can be matched with standard identity recognition characteristics in the identity recognition characteristic database, the condition of system switching is met, and the system is switched from a first system to a second system; the switching system has strong mode confidentiality, does not expose the terminal and has double systems, and meets the use requirements of users.
Drawings
Fig. 1 is a flowchart illustrating a method for switching an operating system according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps S10 and S20 in a method for switching an operating system according to an embodiment of the present invention;
FIG. 3 is a block diagram schematically illustrating a switching device of an operating system according to an embodiment of the present invention;
fig. 4 is a schematic block diagram of a switching device of an operating system, which includes a pressing duration obtaining unit and a pressing duration determining unit according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following describes the implementation of the present invention in detail with reference to specific embodiments.
Fig. 1 shows a flowchart of a switching method of an operating system provided by the present invention, and referring to fig. 1, the method includes: step S30, step S40, step S50, step S60, step S70, step S80, and step S90.
And step S30, acquiring fingerprint information of the user.
It should be noted that fingerprint identification refers to identification by comparing minutiae of different fingerprints. Fingerprint identification technology relates to a plurality of subjects such as image processing, pattern recognition, computer vision, mathematical morphology, wavelet analysis and the like. The fingerprints of each person are different, namely the fingerprints are obviously different among the ten fingers of the same person, so that the fingerprints can be used for identity authentication.
Specifically, the fingerprint collection method mainly includes two types: sliding type and pressing type. The sliding type collection is to slide the finger on the sensor, so that the mobile phone obtains the finger fingerprint image. The sliding type acquisition has the advantages of relatively low cost and capability of acquiring large-area images. However, the acquisition mode has the problem of poor experience, the user needs a continuous and standard sliding motion to realize successful acquisition, and the probability of acquisition failure is greatly increased. The collection method used by a certain brand of mobile phone is subject to scaling due to the existence of a short plate in sliding collection. Push type collection is just pressing on the sensor as the name implies and realizing fingerprint data collection, and this kind of collection mode of course user experience is better, nevertheless the cost is higher than the slip collection, and the technical difficulty is also higher relatively. In addition, because the area of the fingerprint acquired at one time is smaller than that of the fingerprint acquired in a sliding mode, the fingerprint can be acquired for multiple times, and the fingerprint image with a larger area is spliced through splicing. Therefore, advanced algorithm is needed, and the problem that the area of the fingerprint acquired by the sliding-pressing type acquisition is relatively small is solved by using a software algorithm so as to ensure the accuracy of identification.
The collecting device mainly has the following principles:
first, optics formula utilizes light reflection formation of image discernment user's fingerprint, and this type fingerprint module all has certain requirement to service environment's temperature humidity to it is not ideal on the discernment degree of accuracy, and in addition this kind of module can occupy bigger space generally, makes it be difficult to do to some extent at the cell-phone end.
Secondly, the capacitive fingerprint module forms an electric field by utilizing silicon crystal elements and conductive subcutaneous electrolyte, and the pressure difference between the silicon crystal elements and the conductive subcutaneous electrolyte is changed differently due to the fluctuation of the fingerprint, so that accurate fingerprint measurement can be realized. The method has strong adaptability and no special requirement on the use environment, and simultaneously, the silicon crystal element and the related sensing element occupy the space within the acceptable range of the design of the mobile phone, so that the technology is well popularized at the mobile phone end. The existing capacitive fingerprint modules are also divided into a wiping type capacitive fingerprint module and a pressing type capacitive fingerprint module, wherein the former has a small occupied volume but has great disadvantages in the aspects of identification rate and convenience, and therefore manufacturers can directly lock the eyes of all the pressing type capacitive fingerprint modules with more random operation and higher identification rate. The capacitance line identification module mainly comprises a chip, sapphire, a metal ring, a soft board, a carrier plate and the like, wherein the chip is also a sensor part, the sapphire is responsible for serving as a protective layer (other materials are selected by manufacturers as the protective layer, the cost is correspondingly reduced), and the metal ring serves as a trigger device for fingerprint identification.
Third, the radio frequency type, including both radio wave detection and ultrasonic detection, is similar in principle to sonar for detecting seabed material, and detects the specific form of a fingerprint by signal reflection at a specific frequency. The radio frequency fingerprint module technology is that a sensor emits a trace radio frequency signal to penetrate through the epidermal layer of a finger to control and measure the texture of the inner layer so as to obtain the best fingerprint image. The fingerprint module has the greatest advantage that a finger does not need to be in contact with the fingerprint module, so that the appearance of the mobile phone is not greatly influenced. Based on this, the rf fingerprinting module also becomes one of the main development directions of future fingerprinting.
And step S40, analyzing the fingerprint information to obtain the fingerprint characteristics to be matched of the fingerprint information.
Specifically, the features of the fingerprint image are divided into global features and local features, and the global features refer to macroscopic features which can be directly observed and are generally used in a rapid classification and coarse matching stage. The local features refer to several effective features in the fingerprint topological graph, such as whether fingerprint lines are continuous or not, whether directions are consistent or not, and specific to detail features, the detail features are expressed as break points, bifurcation points, intersection points, bridges, rings and the like, which are generally called feature points. The feature points, the feature points and the surrounding ridge lines, etc. contain rich information, such as the types, directions, positions, etc. of the feature points. Feature matching is performed using this information.
The most common feature point extraction algorithms are of two types: firstly, directly extracting feature points from a gray level image; and secondly, extracting characteristic points of the preprocessed and thinned image.
The principle of direct grayscale image fingerprint feature extraction is to use a pattern recognition method to track the line trend on the grayscale image. The lines of the normal area fingerprint should be continuous, and when fracture termination separation occurs, stopping to carry out characteristic point judgment according to the rule. The algorithm mainly comprises several closely-connected modules: (1) and the tracking stepping module is mainly responsible for predicting the next tracking direction and step length and is used for advancing one step along the streak line. (2) And the central point determining module is responsible for determining the ridge central point of the grain line, so that the tracking direction is continuously adjusted and always moves forward along the center of the grain line. (3) And the marking module is used for marking the tracked striae so as to avoid repeated tracking and falling into dead circulation. (4) And the characteristic judging module is responsible for judging whether the tracing reaches the abnormal area of the streak line flow direction or not as the characteristic point and the type of the characteristic point. The specific algorithm is as follows: (1) the direction diagram of the fingerprint image is calculated, and the block direction is generally taken as the direction of the fingerprint. (2) Starting from the initial point, according to the direction information of the fingerprint image, in the normal direction of the point and within a half fingerprint period, the maximum value and the minimum value of the gray distribution are obtained, and the pixel point at the maximum value is taken as a new starting point. (3) Starting from a new starting point, advancing for a certain step length along the direction of a directional diagram of the fingerprint image (the algorithm is firstly tracked according to a fixed step length and then is developed into self-adaptive step length tracking), continuously obtaining the maximum value and the minimum value of gray distribution in the finding direction, and still taking the pixel point at the maximum value as the new starting point. (4) And (4) continuously repeating the step (3) to realize ridge line tracking, stopping tracking until the maximum value of the obtained gray distribution is obviously reduced, is almost equal to or even equal to the minimum value, and indicating that the maximum value has passed through the tail end of the ridge line, namely the end point of the feature point. And stopping tracking if the tracked ridge line intersects with the previously tracked ridge line, and solving the intersection position of the two ridge lines, namely the bifurcation point of the feature point. The algorithm for directly extracting features from a gray level image generally tracks gray level fingerprint lines, searches for the positions of the features according to the tracking result and judges the types of the features. The method omits a complicated fingerprint image preprocessing process, but the algorithm of feature extraction is quite complicated, and the extracted feature information (position, direction and the like) is not accurate enough due to the influence of factors such as noise and the like.
A template matching method based on a thinned image is characterized in that a fingerprint image is subjected to a series of preprocessing such as image normalization, enhancement, binarization, thinning and the like to obtain a thinned fingerprint image, and feature points of the fingerprint image are extracted by constructing a 3 multiplied by 3 neighborhood of pixels.
And step S50, judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal.
Specifically, in the verification mode, fingerprint matching mainly compares whether two groups of feature points conform to the same structural mode; in the recognition mode, the fingerprints are classified first, and then fingerprints consistent with the classes are searched in the fingerprint database according to different classes to carry out final accurate comparison. The matching of minutiae points is generally a pattern formed by comparing feature points of two images. The similarity degree of the two characteristic point patterns is compared by the number of matched characteristic points, and the matching result is obtained by comparing with a preset threshold value.
Therefore, the core idea of the matching algorithm is: the influence of fingerprint displacement, rotation and distortion on the feature point location is eliminated through some coordinate transformation (such as translation, rotation and stretching transformation); and then comparing the related information (position, type, angle and the like) of the fingerprint feature points after coordinate transformation. Due to the influence of various factors, the feature templates obtained by inputting the same fingerprint twice are difficult to be completely the same, so when the features of the input fingerprint are compared with the template features in the fingerprint library to a certain degree of similarity, the two fingerprints are considered to be successfully matched. It is believed that if there are 13 pairs of feature points matching between two fingerprints, a successful match can be concluded, i.e., two fingerprints from the same finger of the same person can be concluded.
In addition, in the actual comparison process, the comparison between two fingerprints is not a rigid, unbiased comparison, but rather a flexible, comparison that allows for some positional and orientational misalignment. Therefore, the matching result is expressed by "degree of matching". When the matching degree is larger than a certain threshold value, the two fingerprints are considered to be matched: when less than the threshold, a mismatch is considered. The matching algorithms are different, the calculation methods of the matching degrees are also different, and the size of the threshold is usually set manually according to factors such as experience, system safety level and the like. When the threshold is larger, the system security increases, but the false rejection rate will increase. When the threshold is smaller, the system ease of use increases, but the false acceptance rate will increase.
And step S60, if the fingerprint feature to be matched is the standard fingerprint feature in the fingerprint feature database prestored in the terminal, acquiring the identity identification information of the user.
Specifically, the identification information includes one or more of voiceprint information, face information, iris information, and body impedance information.
The voiceprint is a sound wave spectrum carrying speech information displayed by an electro-acoustic instrument. Modern scientific research shows that the voiceprint not only has specificity, but also has the characteristic of relative stability. After the adult, the voice of the human can be kept relatively stable and unchanged for a long time. Experiments prove that whether a speaker intentionally imitates the voice and tone of other people or speaks with whisper and whisper, even if the imitation is vivid, the voiceprint of the speaker is different all the time.
The standard voiceprint acquisition equipment can be introduced in the market by reference, is specially researched and developed for a standard voiceprint acquisition scene, adopts an intelligent microphone cluster, supports unidirectional/omnidirectional pickup and various text acquisition modes, ensures the integrity and authenticity of voiceprint information acquisition content, and improves the quality and efficiency of voiceprint acquisition.
Face recognition is a biometric technology for identity recognition based on facial feature information of a person. A series of related technologies, also commonly called face recognition and face recognition, are used to collect images or video streams containing faces by using a camera or a video camera, automatically detect and track the faces in the images, and further recognize the detected faces.
Different face images can be collected through the camera lens, and for example, static images, dynamic images, different positions, different expressions and the like can be well collected. When the user is in the shooting range of the acquisition equipment, the acquisition equipment can automatically search and shoot the face image of the user. In practice, face detection is mainly used for preprocessing of face recognition, namely, the position and size of a face are accurately calibrated in an image. The face image contains abundant pattern features, such as histogram features, color features, template features, structural features, and the like. The face detection is to extract the useful information and to use the features to realize the face detection. The image preprocessing for the human face is a process of processing the image based on the human face detection result and finally serving for feature extraction. The original image acquired by the system is limited by various conditions and random interference, so that the original image cannot be directly used, and the original image needs to be subjected to image preprocessing such as gray scale correction, noise filtering and the like in the early stage of image processing. For the face image, the preprocessing process mainly includes light compensation, gray level transformation, histogram equalization, normalization, geometric correction, filtering, sharpening, and the like of the face image.
Iris recognition technology is based on the iris in the eye for identification. The human eye structure is composed of parts such as the sclera, iris, pupil lens, retina, etc. The iris is an annular segment between the black pupil and the white sclera containing many details characteristic of interlaced spots, filaments, coronaries, stripes, crypts, etc. And the iris will remain unchanged throughout life span after it is formed during the fetal development stage. These features determine the uniqueness of the iris features and also the uniqueness of the identification. Therefore, the iris feature of the eye can be used as an identification target for each person.
The iris image is obtained by shooting the whole eye of a person through a specific camera equipment, transmitting the shot image to image preprocessing software of an iris recognition system, and processing the obtained iris image as follows so as to meet the requirement of extracting iris characteristics: (1) and (4) iris positioning, namely determining the positions of the inner circle, the outer circle and the quadratic curve in the image. Wherein, the inner circle is the boundary between the iris and the pupil, the outer circle is the boundary between the iris and the sclera, and the secondary curve is the boundary between the iris and the upper and lower eyelids. (2) And (4) iris image normalization, namely adjusting the size of the iris in the image to a fixed size set by a recognition system. (3) And (4) image enhancement, namely processing brightness, contrast, smoothness and the like aiming at the normalized image, and improving the recognition rate of the iris information in the image.
And step S70, analyzing the identity recognition information to obtain the identity recognition features to be matched of the identity recognition information.
It should be noted that, for voiceprint recognition, the timbre is the attribute that can reflect the identity information of a person most, and the difference in timbre can be expressed as the difference in energy of different frequency bands in the frequency domain at the level of signal processing, so that by extracting the energy values in different frequency bands, the property of the frequency spectrum in the short-term speech range can be expressed. Generally, the auditory properties of human ears, energy difference of different frequency bands, noise robustness and subsequent calculation are comprehensively considered, proper short-time acoustic features are conveniently designed, and a section of 20-50 millisecond-length speech can be mapped into a section of 39-60 dimensional vector through a series of complicated signal processing level transformation. In order to fully retain the original information in the speech without increasing the computational burden, short-period speech is sequentially taken at intervals of 15-20 milliseconds, and then features are extracted. In the field of voiceprint recognition, including speech recognition, conventional acoustic features include mel-frequency cepstrum coefficients MFCC, perceptual linear prediction coefficients PLP, depth features, deep features, which gradually receive attention in recent years, and energy regularization spectral coefficients, which can be used as voiceprint recognition and are selectable at a feature extraction level and have good performance. In summary, a segment of speech is mapped into a series of vector sets on the time axis, and these sets can become feature sets reflecting speech characteristics after some normalization operations. Next, modeling is performed for forming a voiceprint into features, and a voiceprint recognition system is a typical framework of pattern recognition, in order for a computer to recognize the identity of a user, a target user needs to first provide a section of training speech, and the section of speech is mapped into a voiceprint model of the user through a series of operations such as feature extraction and model training.
The method for extracting the face features is summarized as follows: (1) subspace-based (algebraic features) methods: an image is regarded as a matrix, and the algebraic features of the human face can be extracted by performing matrix transformation or linear projection; (2) the method based on geometric features: the human face is composed of parts such as eyes, a nose, a mouth, a chin and the like, and geometric description of the parts and the structural relationship can be used as important features for recognizing the human face. (3) The neural network method comprises the following steps: the artificial neural network can be used for pattern recognition and is not influenced by pattern deformation due to the inherent parallel operation mechanism and the distributed global storage of the patterns. (4) The deformation model method comprises the following steps: the shape features of the face are described by selecting some sparse reference points at the edges of the face features, then the shape is changed to the average shape of all face images, and texture gray level deformation is carried out according to the deformed shape to form the face image with irrelevant shape. (5) Based on the elastic model matching method: the idea of the elastic model matching method is that some feature points on a human face are used as reference points to form an elastic graph, each reference point stores a string of representative feature vectors, and a hierarchical elastic graph is adopted to remove some redundant nodes to form a sparse human face description structure. The identification is accomplished by elastic matching of the test sample and the feature sample.
For iris recognition, iris images contain rich detail features. If the preprocessed iris image is used as a texture image, many texture analysis methods can be used to extract the iris features. There are three typical methods: (1) gabor filtering: one of the effective strategies to extract texture information from an image is to convolve the image with a band pass filter, where the band pass filter may select a 2d gabor filter. The 2DGabor filter provides maximum resolution in spatial frequency, direction, spatial position, and therefore has good joint localization capability in space and frequency domains. (2) Two-dimensional wavelet transformation: wavelet transform is a common image analysis means and has more applications in texture recognition. A two-dimensional wavelet transform can be viewed as two consecutive one-dimensional wavelet transforms. Two-dimensional wavelet transforms decompose an image into a series of low frequency sub-images. (3) Wavelet transform zero-crossing detection algorithm: and (4) carrying out interval sampling on the iris image by using a concentric circle with the center of the iris as the center of the circle. The two-dimensional iris image is converted into a one-dimensional signal.
And step S80, judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal.
Specifically, for voiceprint feature recognition, in a verification stage, a voice with unknown identity is also mapped into a test feature through a series of operations, the test feature and a target model are subjected to certain similarity calculation to obtain a confidence score, the score is usually compared with an expected value set manually by people and is higher than the expected value, the identity corresponding to the test voice is considered to be matched with the identity of a target user, and verification is passed; otherwise, the test identity is rejected.
For face recognition, the face recognition methods all have similar processes, i.e. all use a classified training data set (face database, each person has many samples) to train, analyze the face detected in the image or video, and determine from two aspects: a measure of whether a target was identified and the confidence with which the target was actually identified, also referred to as a confidence score. The method comprises the following steps: (1) eigenfaces algorithm realizes face recognition, and Eigenfaces are processed through PCA. PCA is the most referenced mathematical concept in computer vision. The essence of PCA is to recognize principal components on a training set (e.g., a database of faces), and to calculate the degree of divergence of the training set (faces detected in an image or frame) from the database and output a value. The smaller the value, the smaller the difference between the face database and the detected face is; a value of 0 indicates a perfect match. (2) The Fisherfaces algorithm implements face recognition, which is a concept derived and developed from PCA that employs more complex logic. Although more computationally intensive, accurate results are easier to obtain than Eigenfaces. (3) The localbinarypattern histogram (LBPH) algorithm implements face recognition, where LBPH roughly (at a very high level) divides the detected face into small cells and compares them to corresponding cells in the model, producing a histogram of the match values for each region. Due to the flexibility of this approach, LBPH is the only face recognition algorithm that allows model sample faces and detected faces to be different in shape and size.
For iris recognition, iris recognition based on extracted iris features is a typical pattern matching problem. Two algorithms that are more commonly used are: (1) hamming distance: after converting the iris texture into effective iris codes, the different iris codes are judged according to the Hamming distance of the iris codes, namely different iris codes are subjected to bitwise XOR comparison. (2) Inverse variance weighted euclidean distance classification: the feature vector of the unknown iris is compared with the trained iris feature vectors of known classes, and the input iris is classified as the K-th iris if and only if the Euclidean distance weighted by the inverse variance of the feature vector of the unknown iris and the K-th feature to the scene is minimum.
Stored in the iris feature database are feature vectors for known iris textures. In the process of iris recognition, firstly, processing and analyzing an iris image to be recognized to obtain an iris feature code, then, comparing the extracted feature code with a feature code template in a database, and finally obtaining a classification result. In order to use the iris for identification, a large iris feature database is needed in the background of the iris identification system, so that the iris feature code can be conveniently stored and inquired.
And step S90, if the identification features to be matched are standard identification features in an identification feature database prestored in the terminal, switching the operating system of the terminal.
Specifically, the first operating system is a normal mode system; the second operating system is a secure mode system for encrypting important data.
Here, the terminal includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a desktop computer, a car computer, a smart watch, a smart bracelet, other wearable smart devices, a POS machine, and the like.
The provided switching method of the operating system acquires the fingerprint information of the user; analyzing according to the fingerprint information to obtain fingerprint features to be matched of the fingerprint information; judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal; if so, acquiring the identity identification information of the user; analyzing according to the identity recognition information to obtain identity recognition features to be matched of the identity recognition information; judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal; and if so, switching the operating system of the terminal. The method comprises the steps that a fingerprint characteristic database and an identity recognition characteristic database are preset in a terminal, when acquired fingerprint information is analyzed to be fingerprint characteristics and can be matched with standard fingerprint characteristics in the fingerprint characteristic database, identity recognition information is acquired, when acquired identity recognition information is analyzed to be identity recognition characteristics and can be matched with standard identity recognition characteristics in the identity recognition characteristic database, the condition of system switching is met, and the system is switched from a first system to a second system; the switching system has strong mode confidentiality, does not expose the terminal and has double systems, and meets the use requirements of users.
Referring to fig. 2, in some embodiments, the method for switching the operating system further includes:
and step S10, acquiring the pressing duration of the fingerprint sensing element of the terminal continuously pressed by the fingerprint of the user.
Specifically, the fingerprint sensing element is disposed between the sensing plane and the circuit board for sensing a fingerprint image of a finger placed on the sensing plane.
And step S20, judging whether the pressing time length exceeds a preset threshold value.
Specifically, if the pressing time period exceeds a preset threshold, step S30 is executed.
It should be noted that the pressing time period exceeding the preset threshold exists as a trigger switch for switching the operating system. The preset threshold may be set to a time period that is not reached regularly, so as to prevent the system switch from being turned on by mistake when the fingerprint sensing element is pressed daily for other reasons.
Referring to fig. 2, in some embodiments, the method for switching the operating system further includes:
and if the fingerprint features to be matched are not the standard fingerprint features in the fingerprint feature database prestored in the terminal, quitting the verification, and maintaining the original system by the terminal.
Referring to fig. 2, in some embodiments, the method for switching the operating system further includes:
and if the identification features to be matched are not the standard identification features in the identification feature database prestored in the terminal, quitting the verification and maintaining the original system by the terminal.
Referring to fig. 3, fig. 3 is a schematic block diagram illustrating a switching device of an operating system according to an embodiment of the present invention; the switching device of the operating system provided by the embodiment of the present application may be configured in a terminal or a server, and is configured to execute the aforementioned switching method of the operating system. The switching device of the operating system comprises:
a first acquisition unit 1 for acquiring fingerprint information of a user;
the first analysis unit 2 is used for analyzing the fingerprint information to obtain the fingerprint features to be matched of the fingerprint information;
the first judging unit 3 is used for judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;
the second obtaining unit 4 is configured to obtain the identity identification information of the user when the first determining unit determines that the fingerprint feature to be matched is a standard fingerprint feature in a fingerprint feature database prestored in the terminal;
the second analysis unit 5 is used for analyzing the identity recognition information to obtain the identity recognition features to be matched of the identity recognition information;
a second judging unit 6, configured to judge whether the identification feature to be matched is a standard identification feature in an identification feature database prestored in the terminal;
and the switching unit 7 is configured to switch the operating system of the terminal when the second judging unit judges that the identity recognition feature is a standard identity recognition feature in an identity recognition feature database prestored in the terminal.
Please refer to fig. 4, the apparatus further includes:
a pressing duration obtaining unit 8, configured to obtain a pressing duration for a user to continuously press a fingerprint sensing element of the terminal;
the pressing duration judging unit 9 is configured to judge whether the pressing duration exceeds a preset threshold, where if the pressing duration judging unit judges that the pressing duration exceeds the preset threshold, the first obtaining unit obtains fingerprint information of the user.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus and the units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The apparatus described above may be implemented in the form of a computer program which is executable on a computer device.
The computer device may be a terminal. The terminal provided by the embodiment of the invention comprises a processor and a memory; the memory may include, among other things, a non-volatile storage medium and an internal memory.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any one of the operating system switching methods.
The internal memory provides an environment for running a computer program in the non-volatile storage medium, which when executed by the processor causes the processor to perform any one of the methods for switching operating systems.
It should be understood that the processor may be a Central Processing Unit (CPU), and the processor may be other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, where the computer program includes program instructions, and the processor executes the program instructions to implement any one of the switching methods of the operating systems provided in the embodiments of the present application. The program may include some or all of the steps in the embodiments of the method for switching the operating system provided by the present invention.
The computer-readable storage medium may be an internal storage unit of the terminal according to the foregoing embodiment, for example, a hard disk or a memory of the terminal. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk provided on the terminal, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card (FlashCard), and the like.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A switching method of an operating system is applied to a terminal, the terminal is provided with at least a first operating system and a second operating system which can be switched back and forth, and the switching method comprises the following steps:
acquiring fingerprint information of a user;
analyzing according to the fingerprint information to obtain the fingerprint characteristics to be matched of the fingerprint information;
judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;
if so, acquiring the identity identification information of the user;
analyzing according to the identity recognition information to obtain identity recognition features to be matched of the identity recognition information;
judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal;
and if so, switching the operating system of the terminal.
2. The method of switching an operating system according to claim 1, wherein the switching method further comprises:
acquiring the pressing duration of the fingerprint sensing element of the terminal continuously pressed by the fingerprint of the user;
judging whether the pressing time length exceeds a preset threshold value or not;
and if so, acquiring the fingerprint information of the user.
3. The method for switching operating systems according to claim 1, wherein the first operating system is a normal mode system; the second operating system is a secure mode system for encrypting important data.
4. The method of claim 1, wherein the identification information comprises one or more of voiceprint information, facial information, iris information, and body impedance information.
5. The method of switching an operating system according to claim 1, wherein the switching method further comprises:
and if the fingerprint features to be matched are not the standard fingerprint features in the fingerprint feature database prestored in the terminal, quitting the verification, and maintaining the original system by the terminal.
6. The method of switching an operating system according to claim 1, wherein the switching method further comprises:
and if the identification features to be matched are not the standard identification features in the identification feature database prestored in the terminal, quitting the verification, and maintaining the original system by the terminal.
7. A switching device of an operating system, applied to a terminal having at least a first operating system and a second operating system that can be switched back and forth, the switching device comprising:
the first acquisition unit is used for acquiring fingerprint information of a user;
the first analysis unit is used for analyzing the fingerprint information to obtain the fingerprint features to be matched of the fingerprint information;
the first judgment unit is used for judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;
the second acquisition unit is used for acquiring the identity identification information of the user when the first judgment unit judges that the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;
the second analysis unit is used for analyzing the identity recognition information to obtain the identity recognition features to be matched of the identity recognition information;
the second judging unit is used for judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal;
and the switching unit is used for switching the operating system of the terminal when the second judging unit judges that the identification features are standard identification features in an identification feature database prestored in the terminal.
8. The switching apparatus of an operating system according to claim 7, wherein said apparatus further comprises:
the pressing duration acquisition unit is used for acquiring the pressing duration of the fingerprint sensing element of the terminal continuously pressed by the fingerprint of the user;
the pressing duration judging unit is used for judging whether the pressing duration exceeds a preset threshold value, and if the pressing duration judging unit judges that the pressing duration exceeds the preset threshold value, the first acquiring unit acquires the fingerprint information of the user.
9. A terminal, characterized in that the terminal comprises a memory and a processor;
the memory stores a computer program;
the processor is configured to execute the computer program and implement the switching method of the operating system according to any one of claims 1 to 6 when executing the computer program.
10. A readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the switching method of an operating system of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010679897.0A CN111880848A (en) | 2020-07-15 | 2020-07-15 | Switching method and device of operating system, terminal and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010679897.0A CN111880848A (en) | 2020-07-15 | 2020-07-15 | Switching method and device of operating system, terminal and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111880848A true CN111880848A (en) | 2020-11-03 |
Family
ID=73151462
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010679897.0A Pending CN111880848A (en) | 2020-07-15 | 2020-07-15 | Switching method and device of operating system, terminal and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111880848A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766759A (en) * | 2021-01-25 | 2021-05-07 | 深圳市广程杰瑞科技有限公司 | Refueling management method and system for logistics enterprises |
| CN115018834A (en) * | 2022-08-08 | 2022-09-06 | 山东光岳九州半导体科技有限公司 | Semiconductor wafer image alignment method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120154413A1 (en) * | 2010-12-21 | 2012-06-21 | Dongwoo Kim | Mobile terminal and method of controlling a mode switching therein |
| CN105335641A (en) * | 2015-10-16 | 2016-02-17 | 宇龙计算机通信科技(深圳)有限公司 | Identity authentication method and device based on fingerprint identification |
| CN105843681A (en) * | 2016-03-24 | 2016-08-10 | 捷开通讯(深圳)有限公司 | Mobile terminal and operating system switching method thereof |
| CN106250741A (en) * | 2016-07-22 | 2016-12-21 | 北京珠穆朗玛移动通信有限公司 | A kind of system switching method and mobile terminal |
| CN106557678A (en) * | 2016-11-09 | 2017-04-05 | 珠海格力电器股份有限公司 | A kind of intelligent terminal's mode switching method and its device |
| CN107621921A (en) * | 2017-08-03 | 2018-01-23 | 广东小天才科技有限公司 | A kind of application program launching method and mobile device |
| CN109815668A (en) * | 2019-01-10 | 2019-05-28 | 北京珠穆朗玛移动通信有限公司 | Dual system method for controlling mobile terminal, mobile terminal and storage medium |
| CN110222492A (en) * | 2019-05-05 | 2019-09-10 | 深圳汇茂科技股份有限公司 | System switching method, device, computer equipment and computer readable storage medium |
-
2020
- 2020-07-15 CN CN202010679897.0A patent/CN111880848A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120154413A1 (en) * | 2010-12-21 | 2012-06-21 | Dongwoo Kim | Mobile terminal and method of controlling a mode switching therein |
| CN105335641A (en) * | 2015-10-16 | 2016-02-17 | 宇龙计算机通信科技(深圳)有限公司 | Identity authentication method and device based on fingerprint identification |
| CN105843681A (en) * | 2016-03-24 | 2016-08-10 | 捷开通讯(深圳)有限公司 | Mobile terminal and operating system switching method thereof |
| CN106250741A (en) * | 2016-07-22 | 2016-12-21 | 北京珠穆朗玛移动通信有限公司 | A kind of system switching method and mobile terminal |
| CN106557678A (en) * | 2016-11-09 | 2017-04-05 | 珠海格力电器股份有限公司 | A kind of intelligent terminal's mode switching method and its device |
| CN107621921A (en) * | 2017-08-03 | 2018-01-23 | 广东小天才科技有限公司 | A kind of application program launching method and mobile device |
| CN109815668A (en) * | 2019-01-10 | 2019-05-28 | 北京珠穆朗玛移动通信有限公司 | Dual system method for controlling mobile terminal, mobile terminal and storage medium |
| CN110222492A (en) * | 2019-05-05 | 2019-09-10 | 深圳汇茂科技股份有限公司 | System switching method, device, computer equipment and computer readable storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766759A (en) * | 2021-01-25 | 2021-05-07 | 深圳市广程杰瑞科技有限公司 | Refueling management method and system for logistics enterprises |
| CN112766759B (en) * | 2021-01-25 | 2022-06-17 | 深圳市广程杰瑞科技有限公司 | Refueling management method and system for logistics enterprises |
| CN115018834A (en) * | 2022-08-08 | 2022-09-06 | 山东光岳九州半导体科技有限公司 | Semiconductor wafer image alignment method |
| CN115018834B (en) * | 2022-08-08 | 2022-10-25 | 山东光岳九州半导体科技有限公司 | Semiconductor wafer image alignment method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107438854B (en) | System and method for performing fingerprint-based user authentication using images captured by a mobile device | |
| CN110326001B (en) | System and method for performing fingerprint-based user authentication using images captured with a mobile device | |
| US9189686B2 (en) | Apparatus and method for iris image analysis | |
| Barpanda et al. | Iris feature extraction through wavelet mel-frequency cepstrum coefficients | |
| CN110287918B (en) | Living body identification method and related product | |
| Majekodunmi et al. | A review of the fingerprint, speaker recognition, face recognition and iris recognition based biometric identification technologies | |
| CN111880848A (en) | Switching method and device of operating system, terminal and readable storage medium | |
| Gawande et al. | Biometric-based security system: Issues and challenges | |
| Gawande et al. | Improving iris recognition accuracy by score based fusion method | |
| Kahlil et al. | Generation of iris codes using 1d log-gabor filter | |
| Hasan et al. | Dual iris matching for biometric identification | |
| Danlami et al. | A framework for iris partial recognition based on Legendre wavelet filter | |
| Walker et al. | Partial iris and recognition as a viable biometric scheme | |
| Basha et al. | Fast multimodal biometric approach using dynamic fingerprint authentication and enhanced iris features | |
| Cui et al. | An appearance-based method for iris detection | |
| Talebi et al. | A novel iris segmentation method based on balloon active contour | |
| Jeon et al. | Performance evaluation of teeth image recognition system based on difference image entropy | |
| Choras | A review of image processing methods and biometric trends for personal authentication and identification | |
| Leon et al. | Fingerprint verification applying invariant moments | |
| Supriya et al. | Efficient iris recognition by fusion of matching scores obtained by lifting DWT and Log-Gabor methods of feature extraction | |
| Jassim et al. | PERFORMANCE AND RELIABILITY FOR IRIS RECOGNITION BASED ON CORRECT SEGMENTATION. | |
| Gladis et al. | SYSTEMATIC DIGITAL SIGNAL PROCESSING APPROACH IN VARIOUS BIOMETRIC IDENTIFICATION. | |
| Dixit et al. | SIFRS: Spoof Invariant Facial Recognition System (A Helping Hand for Visual Impaired People) | |
| Avazpour et al. | Optimization of Human Recognition from the Iris Images using the Haar Wavelet. | |
| Roja et al. | Iris recognition using orthogonal transforms |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |