CN113312946A - Fingerprint image feature extraction method and device and computer readable storage medium - Google Patents
Fingerprint image feature extraction method and device and computer readable storage medium Download PDFInfo
- Publication number
- CN113312946A CN113312946A CN202010125597.8A CN202010125597A CN113312946A CN 113312946 A CN113312946 A CN 113312946A CN 202010125597 A CN202010125597 A CN 202010125597A CN 113312946 A CN113312946 A CN 113312946A
- Authority
- CN
- China
- Prior art keywords
- fingerprint image
- binary
- features
- neural network
- fingerprint
- 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
- 238000000605 extraction Methods 0.000 title claims description 33
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 53
- 230000004913 activation Effects 0.000 claims abstract description 45
- 238000000034 method Methods 0.000 claims abstract description 25
- 238000007781 pre-processing Methods 0.000 claims abstract description 24
- 238000004590 computer program Methods 0.000 claims description 17
- 230000011218 segmentation Effects 0.000 claims description 10
- 230000006870 function Effects 0.000 description 50
- 238000004422 calculation algorithm Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 208000030757 autosomal dominant osteopetrosis Diseases 0.000 description 2
- 238000013145 classification model Methods 0.000 description 2
- 238000004132 cross linking Methods 0.000 description 2
- 238000003709 image segmentation Methods 0.000 description 2
- ZWLUXSQADUDCSB-UHFFFAOYSA-N phthalaldehyde Chemical compound O=CC1=CC=CC=C1C=O ZWLUXSQADUDCSB-UHFFFAOYSA-N 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1347—Preprocessing; Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/757—Matching configurations of points or features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/467—Encoded features or binary features, e.g. local binary patterns [LBP]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Medical Informatics (AREA)
- Human Computer Interaction (AREA)
- Collating Specific Patterns (AREA)
Abstract
The invention relates to a method and a device for extracting characteristics of a fingerprint image and a computer readable storage medium. The method comprises the following steps: the method comprises the following steps: acquiring a fingerprint image; preprocessing the fingerprint image to remove noise in the fingerprint image; extracting binary features of the fingerprint image by using a trained preset convolutional neural network model, wherein the preset convolutional neural network comprises a continuous activation function with a function of simulating a step function; and matching the binary characteristics of the fingerprint image with the binary characteristics of a prestored fingerprint image to obtain a matching result. According to the scheme, the feature output by the preset convolutional neural network model is subjected to binarization processing through an activation function of the preset convolutional neural network model to obtain a binary feature of the fingerprint image, and the binary feature of the fingerprint image is matched with the binary feature of a pre-stored fingerprint image to obtain a matching result, so that the matching efficiency of the binary feature of the fingerprint image is improved.
Description
Technical Field
The invention relates to the field of fingerprint image identification, in particular to a fingerprint image feature extraction method and device and a computer readable storage medium.
Background
The current neural network of the fingerprint image for feature extraction outputs floating point type descriptors, the feature comparison speed is slow, and the platform application requirement with high speed requirement cannot be met. For example, the secondary/tertiary feature points of the image obtained by extracting the features of the fingerprint image by using the feature point-based image matching algorithm such as sift and orb are extremely numerous, and if floating point feature matching is still used, the matching efficiency of the image is extremely low.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method, an apparatus and a computer-readable storage medium for extracting a feature of a fingerprint image to extract a binary feature of the fingerprint image, so as to improve the matching efficiency of the fingerprint image.
A first aspect of the present application provides a method for extracting features of a fingerprint image, the method comprising the steps of:
acquiring a fingerprint image;
preprocessing the fingerprint image to remove noise in the fingerprint image;
extracting binary features of the fingerprint image by using a trained preset convolutional neural network model, wherein the preset convolutional neural network comprises a continuous activation function with a function of simulating a step function; and
and matching the binary characteristics of the fingerprint image with the binary characteristics of a prestored fingerprint image to obtain a matching result.
Preferably, the preset convolutional neural network comprises an input layer, a plurality of convolutional layers for feature extraction, a full-link layer, an activation function and an output layer, and the input layer, the convolutional layers, the full-link layer, the activation function and the output layer are connected in sequence.
Preferably, the extracting the binary feature of the fingerprint image by using the trained preset convolutional neural network model includes:
providing an input channel for the fingerprint image through the input layer; training and extracting the characteristics of the fingerprint image through the convolutional layer; integrating the features extracted by the training of each convolution layer through the full-connection layer; performing binarization processing on the features output by the full connection layer through the activation function, and segmenting the result of the binarization processing according to a preset threshold value to obtain binary features of the fingerprint image; and outputting the binary characteristics through the output layer.
Preferably, the activation function
The preset threshold value is 1/2 which is,
the activation function carries out binarization processing on the features output by the full connection layer and divides the result of the binarization processing according to a preset threshold value to obtain the binary features of the fingerprint image, wherein the constant is set by a user and comprises the following steps:
by passing
And carrying out binarization processing on the features output by the full connection layer and segmenting the result of the binarization processing according to 1/2 to obtain binary features of the fingerprint image.
Preferably, the activation function is
The preset threshold value is set to 0 and,
the activation function carries out binarization processing on the features output by the full connection layer and divides the result of the binarization processing according to a preset threshold value to obtain the binary features of the fingerprint image, wherein the constant is set by a user and comprises the following steps:
by said activation function
And carrying out binarization processing on the features output by the full connection layer and segmenting the result of the binarization processing according to 0 to obtain the binary features of the fingerprint image.
Preferably, the matching the binary feature of the fingerprint image with the binary feature of a pre-stored fingerprint image to obtain a matching result includes:
calculating a Hamming distance value between the binary characteristic of the fingerprint image and the characteristic value of the pre-stored fingerprint image; and
and when the Hamming distance value is smaller than or equal to the preset distance value, the fingerprint image is successfully matched with the pre-stored fingerprint image.
Preferably, the calculating a hamming distance value between the binary feature of the fingerprint image and the feature value of the pre-stored fingerprint image includes:
and carrying out XOR operation on the binary characteristics of the fingerprint image and the characteristic values of the prestored fingerprint image to obtain XOR results, wherein the number of the XOR results is the Hamming distance value between the fingerprint image and the prestored fingerprint image.
Preferably, the preprocessing the fingerprint image to remove noise in the fingerprint image comprises:
performing fingerprint segmentation on the fingerprint image to remove a background area in the fingerprint image;
fingerprint enhancement is carried out on the fingerprint image so as to remove cross connection, break points and fuzzy parts in the fingerprint image; and
and thinning the fingerprint image to delete edge pixels of Chinese lines in the fingerprint image.
A second aspect of the present application provides a feature extraction apparatus of a fingerprint image, the apparatus comprising:
the acquisition module is used for acquiring a fingerprint image;
the preprocessing module is used for preprocessing the fingerprint image to remove noise in the fingerprint image;
the feature extraction module is used for extracting binary features of the fingerprint image by utilizing a trained preset convolutional neural network model, wherein the preset convolutional neural network comprises a continuous activation function with a function of simulating a step function; and
and the matching module is used for matching the binary characteristics of the fingerprint image with the binary characteristics of a pre-stored fingerprint image to obtain a matching result.
A third aspect of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of feature extraction of the fingerprint image.
According to the scheme, the feature output by the preset convolutional neural network model is subjected to binarization processing through an activation function of the preset convolutional neural network model to obtain a binary feature of the fingerprint image, and the binary feature of the fingerprint image is matched with the binary feature of a pre-stored fingerprint image to obtain a matching result, so that the matching efficiency of the binary feature of the fingerprint image is improved.
Drawings
Fig. 1 is a flowchart of a feature extraction method for a fingerprint image according to an embodiment of the present invention.
Fig. 2 is a block diagram of a feature extraction device for a fingerprint image according to an embodiment of the present invention.
Fig. 3 is a schematic view of an electronic device of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Preferably, the fingerprint image feature extraction method is applied to one or more electronic devices. The electronic device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The electronic device may be a desktop computer, a notebook computer, a tablet computer, a cloud server, or other computing device. The device can be in man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
Example 1
Fig. 1 is a flowchart of a feature extraction method for a fingerprint image according to an embodiment of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
Referring to fig. 1, the method for extracting the features of the fingerprint image specifically includes the following steps:
and step S101, acquiring a fingerprint image.
In this embodiment, a fingerprint image is collected by a fingerprint collector. In this embodiment, the fingerprint acquisition device is an optical fingerprint acquisition device, a thermal fingerprint identification device or a biological radio frequency fingerprint identification device.
And step S102, preprocessing the fingerprint image to remove noise in the fingerprint image.
In this embodiment, the preprocessing the fingerprint image to remove the noise in the fingerprint image includes: performing fingerprint segmentation on the fingerprint image to remove a background area in the fingerprint image. In particular embodiments, the fingerprint image may be subjected to fingerprint segmentation using a threshold-based image segmentation algorithm to remove background regions in the fingerprint image.
In one embodiment, the preprocessing the fingerprint image to remove noise in the fingerprint image comprises: fingerprint enhancement is performed on the fingerprint image to remove cross-linking, break points and blurred portions in the fingerprint image. In a specific embodiment, the fingerprint image is subjected to fingerprint segmentation by an stft (short Time Fourier transform) fingerprint enhancement algorithm, so as to remove a background region in the fingerprint image.
In one embodiment, the preprocessing the fingerprint image to remove noise in the fingerprint image comprises: and thinning the fingerprint image to delete edge pixels of Chinese lines in the fingerprint image. In a specific embodiment, the fingerprint image is subjected to thinning processing through an OPTA thinning algorithm so as to delete edge pixels of Chinese lines in the fingerprint image.
In one embodiment, the preprocessing the fingerprint image to remove noise in the fingerprint image comprises: performing fingerprint segmentation on the fingerprint image to remove a background area in the fingerprint image; fingerprint enhancement is carried out on the fingerprint image so as to remove cross connection, break points and fuzzy parts in the fingerprint image; and thinning the fingerprint image to delete the edge pixels of the Chinese lines in the fingerprint image.
Step S103, extracting the binary features of the fingerprint image by using a trained preset convolutional neural network model, wherein the preset convolutional neural network comprises a continuous activation function with a simulatable step function.
In this embodiment, the predetermined convolutional neural network includes an input layer, a plurality of convolutional layers for feature extraction, a full link layer, an activation function, and an output layer. The input layer, the convolution layer, the full connection layer, the activation function and the output layer are connected in sequence. In this embodiment, the "extracting the binary feature of the fingerprint image by using the trained preset convolutional neural network model" includes: providing an input channel for a fingerprint image through the input layer; training and extracting the characteristics of the fingerprint image through a convolutional layer; integrating the features extracted by the training of each convolution layer through the full-connection layer; performing binarization processing on the features output by the full connection layer through the activation function, and segmenting the result of the binarization processing according to a preset threshold value to obtain binary features of the fingerprint image; and outputting the binary characteristics through the output layer.
In this embodiment, the activation function is
The preset threshold value is 1/2 which is,
a constant set for the user. The activation function carries out binarization processing on the features output by the full connection layer and divides the result of the binarization processing according to a preset threshold value to obtain the binary features of the fingerprint image, and the activation function comprises the following steps: by said activation function
And performing binarization processing on the features output by the full connection layer and segmenting the result of the binarization processing according to 1/2 to obtain binary features of the fingerprint image. In the present embodiment, the set constant is gradually increased
When the function is activated
Close to step function
In another embodiment, the activation function is
The preset threshold value is set to 0 and,
a constant set for the user. The activation function carries out binarization processing on the features output by the full connection layer and divides the result of the binarization processing according to a preset threshold value to obtain the binary features of the fingerprint image, and the activation function comprises the following steps: by said activation function
And carrying out binarization processing on the features output by the full connection layer and segmenting the result of the binarization processing according to 0 to obtain the binary features of the fingerprint image. In the present embodiment, the set constant is gradually increased
When the function is activated
Close to step function
In this embodiment, the method further includes: and training the preset convolutional neural network model. Specifically, when a preset convolutional neural network model is trained, a preset number of fingerprint image samples may be stored in the server 2, and the fingerprint image samples are classified by a user; for example, 1000 fingerprint image samples may be prepared, then the 1000 fingerprint image samples are classified according to the users to which the fingerprint image samples belong, and each classified fingerprint image sample is subjected to binary feature calibration. After the prepared fingerprint image samples with the preset number are classified, the preset convolutional neural network model can be used as a classification model, the human face fingerprint image samples are used as training samples and input into the preset convolutional neural network model for training, and the weight parameters of the connection between the nodes on the basic layers of the preset convolutional neural network model are adjusted according to the classification result output by the preset convolutional neural network model. After the preset convolutional neural network model is adjusted every time and trained based on the input training samples, the output classification result is compared with the classification result calibrated by the user, and the accuracy is gradually improved. Meanwhile, a user may preset an accuracy threshold, and in the continuous adjustment process, if the classification result output by the preset convolutional neural network model is compared with the classification result calibrated by the user, and the accuracy reaches the preset accuracy threshold, the weight parameters connected between the base layer nodes in the preset convolutional neural network model are all the optimal weight parameters, and the preset convolutional neural network model may be considered to be trained completely. In this embodiment, after the training of the preset convolutional neural network model is completed, the trained preset convolutional neural network model is used to extract the binary features of the fingerprint image input into the preset convolutional neural network model.
And step S104, matching the binary characteristics of the fingerprint image with the binary characteristics of a pre-stored fingerprint image to obtain a matching result.
In this embodiment, the "matching the binary feature of the fingerprint image with the binary feature of a pre-stored fingerprint image to obtain a matching result" includes: calculating a Hamming distance value between the binary characteristic of the fingerprint image and a characteristic value of a prestored fingerprint image; and when the Hamming distance value is smaller than or equal to the preset distance value, the fingerprint image is successfully matched with the prestored fingerprint image.
In this embodiment, the calculating the hamming distance value between the binary feature of the fingerprint image and the feature value of the pre-stored fingerprint image includes: and carrying out XOR operation on the binary characteristics of the fingerprint image and the characteristic values of the pre-stored fingerprint images to obtain XOR results, wherein the number of the XOR results is the Hamming distance value between the fingerprint image and the pre-stored fingerprint images.
In this embodiment, the binary characteristic of the fingerprint image can be obtained by performing binarization processing on the features output by the full connection layer through the activation function of the preset convolutional neural network model and segmenting the result of the binarization processing according to a preset threshold, and the binary characteristic of the fingerprint image is matched with the binary characteristic of a pre-stored fingerprint image to obtain a matching result, so that the matching efficiency of the binary characteristic of the fingerprint image is improved.
Example 2
Fig. 2 is a block diagram of a feature extraction device 40 for a fingerprint image according to an embodiment of the present invention.
In some embodiments, the feature extraction device 40 of the fingerprint image is implemented in an electronic device. The feature extraction means 40 of the fingerprint image may comprise a plurality of functional modules consisting of program code segments. The program code of the various program segments in the fingerprint image feature extraction device 40 may be stored in a memory and executed by at least one processor to perform the functions of feature extraction of a fingerprint image.
In this embodiment, the fingerprint image feature extraction device 40 may be divided into a plurality of functional modules according to the functions performed by the device. Referring to fig. 2, the fingerprint image feature extraction apparatus 40 may include an acquisition module 401, a preprocessing module 402, a feature extraction module 403, and a matching module 404. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In some embodiments, the functionality of the modules will be described in greater detail in subsequent embodiments.
The acquisition module 401 acquires a fingerprint image.
In this embodiment, the acquiring module 401 acquires a fingerprint image through a fingerprint acquirer. In this embodiment, the fingerprint acquisition device is an optical fingerprint acquisition device, a thermal fingerprint identification device or a biological radio frequency fingerprint identification device.
The pre-processing module 402 pre-processes the fingerprint image to remove noise in the fingerprint image.
In this embodiment, the preprocessing module 402 performs fingerprint segmentation on the fingerprint image to remove a background area in the fingerprint image. In a specific embodiment, the preprocessing module 402 may perform fingerprint segmentation on the fingerprint image to remove background regions in the fingerprint image by using a threshold-based image segmentation algorithm.
In an embodiment, the preprocessing module 402 performs fingerprint enhancement on the fingerprint image to remove cross-linking, break points, and blurred portions of the fingerprint image. In a specific embodiment, the preprocessing module 402 performs fingerprint segmentation on a fingerprint image by using an stft (short Time Fourier transform) fingerprint enhancement algorithm, so as to remove a background region in the fingerprint image.
In one embodiment, the preprocessing module 402 refines the fingerprint image to remove edge pixels of lines in the fingerprint image. In a specific embodiment, the preprocessing module 402 performs thinning processing on the fingerprint image through an OPTA thinning algorithm to delete edge pixels of a chinese line in the fingerprint image.
In one embodiment, the preprocessing module 402 performs fingerprint segmentation on the fingerprint image to remove background regions in the fingerprint image; fingerprint enhancement is carried out on the fingerprint image so as to remove cross connection, break points and fuzzy parts in the fingerprint image; and thinning the fingerprint image to delete the edge pixels of the Chinese lines in the fingerprint image.
The feature extraction module 403 extracts binary features of the fingerprint image by using a trained preset convolutional neural network model, where the preset convolutional neural network includes a continuous activation function having a function of an analog step function.
In this embodiment, the predetermined convolutional neural network includes an input layer, a plurality of convolutional layers for feature extraction, a full link layer, an activation function, and an output layer. The input layer, the convolution layer, the full connection layer, the activation function and the output layer are connected in sequence. In this embodiment, the feature extraction module 403 provides an input channel for the fingerprint image through the input layer; training and extracting the characteristics of the fingerprint image through a convolutional layer; integrating the features extracted by the training of each convolution layer through the full-connection layer; performing binarization processing on the features output by the full connection layer through the activation function, and segmenting the result of the binarization processing according to a preset threshold value to obtain binary features of the fingerprint image; and outputting the binary characteristics through the output layer.
In this embodiment, the activation function is
The preset threshold value is 1/2 which is,
a constant set for the user. The feature extraction module 403 passes the activation function
And carrying out binarization processing on the features output by the full connection layer and segmenting the result of the binarization processing according to 1/2 to obtain the two features of the fingerprint image. In the present embodiment, the set constant is gradually increased
When is in use, the
Close to step function
In another embodiment, the activation function is
The preset threshold value is set to 0 and,
constants set for the user, e.g.
Is an integer greater than 1. The feature extraction module 403 passes the activation function
And carrying out binarization processing on the features output by the full connection layer and segmenting the result of the binarization processing according to 0 to obtain the binary features of the fingerprint image. In the present embodiment, the set constant is gradually increased
When is in use, the
Close to step function
In this embodiment, the feature extraction module 403 is further configured to train the preset convolutional neural network model. Specifically, when a preset convolutional neural network model is trained, a preset number of fingerprint image samples may be stored in the server 2, and the fingerprint image samples are classified by a user; for example, 1000 fingerprint image samples may be prepared, then the 1000 fingerprint image samples are classified according to the users to which the fingerprint image samples belong, and each classified fingerprint image sample is subjected to binary feature calibration. After the prepared fingerprint image samples with the preset number are classified, the preset convolutional neural network model can be used as a classification model, the human face fingerprint image samples are used as training samples and input into the preset convolutional neural network model for training, and the weight parameters of the connection between the nodes on the basic layers of the preset convolutional neural network model are adjusted according to the classification result output by the preset convolutional neural network model. After the preset convolutional neural network model is adjusted every time and trained based on the input training samples, the output classification result is compared with the classification result calibrated by the user, and the accuracy is gradually improved. Meanwhile, a user may preset an accuracy threshold, and in the continuous adjustment process, if the classification result output by the preset convolutional neural network model is compared with the classification result calibrated by the user, and the accuracy reaches the preset accuracy threshold, the weight parameters connected between the base layer nodes in the preset convolutional neural network model are all the optimal weight parameters, and the preset convolutional neural network model may be considered to be trained completely. In this embodiment, after the training of the preset convolutional neural network model is completed, the trained preset convolutional neural network model is used to extract the binary features of the fingerprint image input into the preset convolutional neural network model.
The matching module 404 matches the binary features of the fingerprint image with the binary features of a pre-stored fingerprint image to obtain a matching result.
In this embodiment, the matching module 404 calculates a hamming distance value between the binary feature of the fingerprint image and the feature value of the pre-stored fingerprint image, and when the hamming distance value is less than or equal to the preset distance value, the fingerprint image and the pre-stored fingerprint image are successfully matched.
In this embodiment, the matching module 404 is configured to perform an exclusive or operation on the binary features of the fingerprint image and the feature values of the pre-stored fingerprint image to obtain an exclusive or result, where the number of the exclusive or result is a hamming distance value between the fingerprint image and the pre-stored fingerprint image.
In this embodiment, the matching module 404 performs binarization processing on the features output by the full connection layer through the activation function of the preset convolutional neural network model, and segments the result of the binarization processing according to a preset threshold value to obtain a binary feature of the fingerprint image, and matches the binary feature of the fingerprint image with a binary feature of a pre-stored fingerprint image to obtain a matching result, so as to improve the matching efficiency of the binary feature of the fingerprint image.
Example 3
Fig. 3 is a schematic diagram of an electronic device 6 according to an embodiment of the invention.
The electronic device 6 comprises a memory 61, a processor 62 and a computer program 63 stored in the memory 61 and executable on the processor 62. The processor 62, when executing the computer program 63, implements the steps in the above-described embodiment of the fingerprint image feature extraction method, such as steps S101 to S104 shown in fig. 1. Alternatively, the processor 62 executes the computer program 63 to implement the functions of the modules/units in the above-mentioned fingerprint image feature extraction apparatus embodiment, such as the modules 401 to 404 in fig. 2.
Illustratively, the computer program 63 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 62 to carry out the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 63 in the electronic device 6. For example, the computer program 63 may be divided into an obtaining module 401, a preprocessing module 402, a feature extraction module 403, and a matching module 404 in fig. 2, and the specific functions of each module are described in the second embodiment.
The electronic device 6 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the electronic device 6, and does not constitute a limitation of the electronic device 6, and may include more or less components than those shown, or combine certain components, or different components, for example, the electronic device 6 may further include an input-output device, a network access device, a bus, etc.
The Processor 62 may be a Central Processing Unit (CPU), 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. A general purpose processor may be a microprocessor or the processor 62 may be any conventional processor or the like, the processor 62 being the control center for the electronic device 6, with various interfaces and lines connecting the various parts of the overall electronic device 6.
The memory 61 may be used for storing the computer programs 63 and/or modules/units, and the processor 62 may implement various functions of the electronic device 6 by running or executing the computer programs and/or modules/units stored in the memory 61 and calling data stored in the memory 61. The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the stored data area may store data (such as audio data, a phonebook, etc.) created according to the use of the electronic device 6, and the like. In addition, the memory 61 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The integrated modules/units of the electronic device 6, if implemented in the form of software functional modules and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the steps of the above-described embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
In the embodiments provided in the present invention, it should be understood that the disclosed electronic device and method can be implemented in other ways. For example, the above-described embodiments of the electronic device are merely illustrative, and for example, the division of the modules is only one logical functional division, and there may be other divisions when the actual implementation is performed.
In addition, each functional module in each embodiment of the present invention may be integrated into the same processing module, or each module may exist alone physically, or two or more modules may be integrated into the same module. The integrated module can be realized in a hardware form, and can also be realized in a form of hardware and a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is to be understood that the word "comprising" does not exclude other modules or steps, and the singular does not exclude the plural. Several modules or electronic devices recited in the electronic device claims may also be implemented by one and the same module or electronic device by means of software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for extracting features of a fingerprint image, the method comprising the steps of:
acquiring a fingerprint image;
preprocessing the fingerprint image to remove noise in the fingerprint image;
extracting binary features of the fingerprint image by using a trained preset convolutional neural network model, wherein the preset convolutional neural network comprises a continuous activation function with a function of simulating a step function; and
and matching the binary characteristics of the fingerprint image with the binary characteristics of a prestored fingerprint image to obtain a matching result.
2. The method for extracting features of a fingerprint image according to claim 1, wherein the predetermined convolutional neural network comprises an input layer, a plurality of convolutional layers for feature extraction, a fully-connected layer, an activation function, and an output layer, and the input layer, the convolutional layers, the fully-connected layer, the activation function, and the output layer are connected in sequence.
3. The method for extracting features of a fingerprint image according to claim 2, wherein the extracting binary features of the fingerprint image by using the trained pre-configured convolutional neural network model comprises:
providing an input channel for the fingerprint image through the input layer; training and extracting the characteristics of the fingerprint image through the convolutional layer; integrating the features extracted by the training of each convolution layer through the full-connection layer; performing binarization processing on the features output by the full connection layer through the activation function, and segmenting the result of the binarization processing according to a preset threshold value to obtain binary features of the fingerprint image; and outputting the binary feature through the output layer.
4. The method for extracting features of a fingerprint image according to claim 3, wherein said activation function is
The preset threshold value is 1/2 which is,
the activation function carries out binarization processing on the features output by the full connection layer and divides the result of the binarization processing according to a preset threshold value to obtain the binary features of the fingerprint image, wherein the constant is set by a user and comprises the following steps:
by passing
And carrying out binarization processing on the features output by the full connection layer and segmenting the result of the binarization processing according to 1/2 to obtain binary features of the fingerprint image.
5. The method for extracting features of a fingerprint image according to claim 3, wherein said activation function is
The preset threshold value is set to 0 and,
the activation function carries out binarization processing on the features output by the full connection layer and divides the result of the binarization processing according to a preset threshold value to obtain the binary features of the fingerprint image, wherein the constant is set by a user and comprises the following steps:
by said activation function
And carrying out binarization processing on the features output by the full connection layer and segmenting the result of the binarization processing according to 0 to obtain the binary features of the fingerprint image.
6. The method for extracting features of a fingerprint image according to claim 1, wherein the matching the binary features of the fingerprint image with the binary features of a pre-stored fingerprint image to obtain a matching result comprises:
calculating a Hamming distance value between the binary characteristic of the fingerprint image and the characteristic value of the pre-stored fingerprint image; and
and when the Hamming distance value is smaller than or equal to the preset distance value, the fingerprint image is successfully matched with the pre-stored fingerprint image.
7. The method for extracting features of a fingerprint image according to claim 6, wherein said calculating hamming distance values of binary features of the fingerprint image and feature values of the pre-stored fingerprint image comprises:
and carrying out XOR operation on the binary characteristics of the fingerprint image and the characteristic values of the prestored fingerprint image to obtain XOR results, wherein the number of the XOR results is the Hamming distance value between the fingerprint image and the prestored fingerprint image.
8. The method of extracting features of a fingerprint image according to claim 1, wherein preprocessing the fingerprint image to remove noise in the fingerprint image comprises:
performing fingerprint segmentation on the fingerprint image to remove a background area in the fingerprint image;
fingerprint enhancement is carried out on the fingerprint image so as to remove cross connection, break points and fuzzy parts in the fingerprint image; and
and thinning the fingerprint image to delete edge pixels of Chinese lines in the fingerprint image.
9. An apparatus for extracting a feature of a fingerprint image, the apparatus comprising:
the acquisition module is used for acquiring a fingerprint image;
the preprocessing module is used for preprocessing the fingerprint image to remove noise in the fingerprint image;
the feature extraction module is used for extracting binary features of the fingerprint image by utilizing a trained preset convolutional neural network model, wherein the preset convolutional neural network comprises a continuous activation function with a function of simulating a step function; and
and the matching module is used for matching the binary characteristics of the fingerprint image with the binary characteristics of a pre-stored fingerprint image to obtain a matching result.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements a method of feature extraction of a fingerprint image as claimed in any one of claims 1 to 8.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010125597.8A CN113312946A (en) | 2020-02-27 | 2020-02-27 | Fingerprint image feature extraction method and device and computer readable storage medium |
| TW109108509A TWI754241B (en) | 2020-02-27 | 2020-03-13 | A method, a device for extracting features of fingerprint images and computer-readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010125597.8A CN113312946A (en) | 2020-02-27 | 2020-02-27 | Fingerprint image feature extraction method and device and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113312946A true CN113312946A (en) | 2021-08-27 |
Family
ID=77370442
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010125597.8A Pending CN113312946A (en) | 2020-02-27 | 2020-02-27 | Fingerprint image feature extraction method and device and computer readable storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113312946A (en) |
| TW (1) | TWI754241B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116311389A (en) * | 2022-08-18 | 2023-06-23 | 荣耀终端有限公司 | Fingerprint identification method and device |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080085039A1 (en) * | 2005-12-07 | 2008-04-10 | Electronics And Telecommunications Research Institute | Fingerprint matching method and apparatus using fingerprint minutiae and fingerprint binary images |
| CN103971342A (en) * | 2014-05-21 | 2014-08-06 | 厦门美图之家科技有限公司 | Image noisy point detection method based on convolution neural network |
| CN107844753A (en) * | 2017-10-20 | 2018-03-27 | 珠海习悦信息技术有限公司 | Pedestrian in video image recognition methods, device, storage medium and processor again |
| CN109492589A (en) * | 2018-11-13 | 2019-03-19 | 重庆工程职业技术学院 | The recognition of face working method and intelligent chip merged by binary features with joint stepped construction |
| CN109886127A (en) * | 2019-01-23 | 2019-06-14 | 平安科技（深圳）有限公司 | Fingerprint identification method and terminal device |
| CN109936745A (en) * | 2017-11-21 | 2019-06-25 | 辉达公司 | For improving the method and system of the decompression of original video data |
| CN110188227A (en) * | 2019-05-05 | 2019-08-30 | 华南理工大学 | A kind of hashing image search method based on deep learning and low-rank matrix optimization |
| CN110517329A (en) * | 2019-08-12 | 2019-11-29 | 北京邮电大学 | A kind of deep learning method for compressing image based on semantic analysis |
| CN110533603A (en) * | 2019-07-24 | 2019-12-03 | 宁德市公路局 | A kind of point cloud noise-reduction method based on entropy and convolutional neural networks |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103294987A (en) * | 2012-03-05 | 2013-09-11 | 天津华威智信科技发展有限公司 | Fingerprint matching method and fingerprint matching implementation mode |
| US10339362B2 (en) * | 2016-12-08 | 2019-07-02 | Veridium Ip Limited | Systems and methods for performing fingerprint based user authentication using imagery captured using mobile devices |
| CN109063572A (en) * | 2018-07-04 | 2018-12-21 | 南京信息工程大学 | It is a kind of based on multiple dimensioned and multireel lamination Fusion Features fingerprint activity test methods |
| CN108960214A (en) * | 2018-08-17 | 2018-12-07 | 中控智慧科技股份有限公司 | Fingerprint enhancement binarization method, device, equipment, system and storage medium |
| CN109934180B (en) * | 2019-03-18 | 2021-06-01 | Oppo广东移动通信有限公司 | Fingerprint identification method and related device |
| CN110765857A (en) * | 2019-09-12 | 2020-02-07 | 敦泰电子（深圳）有限公司 | Fingerprint identification method, chip and electronic device |
-
2020
- 2020-02-27 CN CN202010125597.8A patent/CN113312946A/en active Pending
- 2020-03-13 TW TW109108509A patent/TWI754241B/en active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080085039A1 (en) * | 2005-12-07 | 2008-04-10 | Electronics And Telecommunications Research Institute | Fingerprint matching method and apparatus using fingerprint minutiae and fingerprint binary images |
| CN103971342A (en) * | 2014-05-21 | 2014-08-06 | 厦门美图之家科技有限公司 | Image noisy point detection method based on convolution neural network |
| CN107844753A (en) * | 2017-10-20 | 2018-03-27 | 珠海习悦信息技术有限公司 | Pedestrian in video image recognition methods, device, storage medium and processor again |
| CN109936745A (en) * | 2017-11-21 | 2019-06-25 | 辉达公司 | For improving the method and system of the decompression of original video data |
| CN109492589A (en) * | 2018-11-13 | 2019-03-19 | 重庆工程职业技术学院 | The recognition of face working method and intelligent chip merged by binary features with joint stepped construction |
| CN109886127A (en) * | 2019-01-23 | 2019-06-14 | 平安科技（深圳）有限公司 | Fingerprint identification method and terminal device |
| CN110188227A (en) * | 2019-05-05 | 2019-08-30 | 华南理工大学 | A kind of hashing image search method based on deep learning and low-rank matrix optimization |
| CN110533603A (en) * | 2019-07-24 | 2019-12-03 | 宁德市公路局 | A kind of point cloud noise-reduction method based on entropy and convolutional neural networks |
| CN110517329A (en) * | 2019-08-12 | 2019-11-29 | 北京邮电大学 | A kind of deep learning method for compressing image based on semantic analysis |
Non-Patent Citations (1)
| Title |
|---|
| 青少年编程备考: "如何利用 C# 实现 Delta 学习规则？", pages 1 - 12, Retrieved from the Internet <URL:https://blog.csdn.net/LSGO_MYP/article/details/88716481> * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116311389A (en) * | 2022-08-18 | 2023-06-23 | 荣耀终端有限公司 | Fingerprint identification method and device |
| CN116311389B (en) * | 2022-08-18 | 2023-12-12 | 荣耀终端有限公司 | Fingerprint identification method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| TWI754241B (en) | 2022-02-01 |
| TW202133041A (en) | 2021-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107944020B (en) | Face image searching method and device, computer device and storage medium | |
| CN116403094B (en) | Embedded image recognition method and system | |
| CN108717744B (en) | Method and device for identifying seal serial number on financial document and terminal equipment | |
| CN112597978B (en) | Fingerprint matching method and device, electronic equipment and storage medium | |
| CN112783825A (en) | Data archiving method, data archiving device, computer device and storage medium | |
| CN111353109A (en) | Malicious domain name identification method and system | |
| US20240037914A1 (en) | Machine learning method and computing device for art authentication | |
| Xu et al. | Multi‐pyramid image spatial structure based on coarse‐to‐fine pyramid and scale space | |
| CN113312946A (en) | Fingerprint image feature extraction method and device and computer readable storage medium | |
| CN116958503B (en) | Image processing-based sludge drying grade identification method and system | |
| CN112149708A (en) | Data model selection optimization method and device, computer device and storage medium | |
| CN111949793B (en) | User intention recognition method and device and terminal equipment | |
| CN110210425B (en) | Face recognition method and device, electronic equipment and storage medium | |
| CN112257689A (en) | Training and recognition method of face recognition model, storage medium and related equipment | |
| Gong et al. | Automatic segmentation of the fine structures of sunspots in high-resolution solar images | |
| CN111507254A (en) | Method, device, terminal and non-transitory computer-readable storage medium for making word-by-word lyrics | |
| CN111767710B (en) | Indonesia emotion classification method, device, equipment and medium | |
| CN115546554A (en) | Sensitive image identification method, device, equipment and computer readable storage medium | |
| CN115457975A (en) | Method and device for detecting baby crying and coughing, storage medium and terminal equipment | |
| CN112037814B (en) | Audio fingerprint extraction method and device, electronic equipment and storage medium | |
| Szymkowski et al. | A novel approach to fingerprint identification using method of sectorization | |
| CN109614854B (en) | Video data processing method and device, computer device and readable storage medium | |
| CN113093967A (en) | Data generation method, data generation device, computer device, and storage medium | |
| CN114490929A (en) | Bidding information acquisition method and device, storage medium and terminal equipment | |
| CN113077469A (en) | Sketch image semantic segmentation method and device, terminal device and storage medium |
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 |