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WO2018151357A1 - Procédé de reconnaissance de visage humain basé sur un filtre de gabor multicanal amélioré - Google Patents

Procédé de reconnaissance de visage humain basé sur un filtre de gabor multicanal amélioré Download PDF

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Publication number
WO2018151357A1
WO2018151357A1 PCT/KR2017/001886 KR2017001886W WO2018151357A1 WO 2018151357 A1 WO2018151357 A1 WO 2018151357A1 KR 2017001886 W KR2017001886 W KR 2017001886W WO 2018151357 A1 WO2018151357 A1 WO 2018151357A1
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Prior art keywords
lbp
image
images
gabor filter
face recognition
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English (en)
Korean (ko)
Inventor
이응주
이석환
왕계원
호앙응옥
동후민넷
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Industry Academic Cooperation Foundation of Tongmyong University
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Industry Academic Cooperation Foundation of Tongmyong University
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G06V40/164—Detection; Localisation; Normalisation using holistic features
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V10/443—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 by matching or filtering
    • G06V10/446—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 by matching or filtering using Haar-like filters, e.g. using integral image techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V10/469—Contour-based spatial representations, e.g. vector-coding
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G06V10/507—Summing image-intensity values; Histogram projection analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G06T2207/30201—Face

Definitions

  • the present invention relates to an improved multi-channel Gabor filter-based human face recognition method. More specifically, the Gabor filter and the CS-LBP in order to reduce the noise robust feature extraction and the high dimensionality of the extracted facial feature points
  • the present invention relates to an improved multi-channel Gabor filter-based human face recognition method for human face recognition combining center-symmetric local binary patterns.
  • the present invention is to solve the above problems, by combining the Gabor filter and the center-symmetric local binary patterns (CS-LBP), to extract the robust feature point extraction and high dimensionality of the extracted facial feature point
  • An object of the present invention is to provide an improved multi-faceted Gabor filter-based human face recognition method.
  • the present invention reduces the dimension of the feature image by combining Gabor feature images in different directions and scales, and improves human face recognition based on multi-channel Gabor filter for extracting low-dimensional facial feature points based on CS-LBP from feature images. It is to provide a method.
  • the present invention provides an improved multi-channel for achieving high accuracy of face recognition while reducing feature dimensions, storage space, and computation time through the Gabor filter and CS-LBP combining method, compared to the conventional Gabor filter and LBP approach. It is to provide a Gabor filter-based human face recognition method.
  • an improved multi-channel Gabor filter-based human face recognition method includes an input of N face (N is a natural number of 2 or more).
  • Output is an image of a cascading particle histogram feature vector representation of the statistics.
  • Feature image A first step of obtaining; bracket
  • a fifth step of extracting a face image feature vector after the feature extraction of the first to fourth steps characterized in that further comprises,
  • the first step is a face image Step 1-1 of obtaining a heirloom amplitude spectrum by performing heirloom conversion; For the amplitude spectrum of n different by overlap, Obtaining a first step 1-2; And for each CS-LBP code, an image Obtaining the first 1-3 steps; Characterized in that it comprises a.
  • a Gabor filter and a center-symmetric local binary patterns are combined to extract feature points that are robust to noise and extracted facial feature points. It provides the effect of reducing high dimensionality.
  • the improved multi-channel Gabor filter-based human face recognition method reduces the dimension of the feature image by combining the Gabor feature images in different directions and scales, and based on CS-LBP based low dimension from the feature image. Provides the effect of extracting facial feature points.
  • the improved multi-channel Gabor filter-based human face recognition method is characterized by a feature dimension, a storage space, and a combination of the Gabor filter and the CS-LBP method, compared to the conventional Gabor filter and LBP approach. It reduces the computation time and at the same time provides the effect of high accuracy of face recognition.
  • FIG. 1 is a diagram illustrating a feature extraction algorithm module 100 in which an improved multi-channel Gabor filter-based human face recognition method is performed according to an embodiment of the present invention.
  • FIG. 2 illustrates an image of a Gabor amplitude spectrum used in an improved multi-channel Gabor filter-based human face recognition method according to an embodiment of the present invention.
  • FIG. 3 is a diagram illustrating an improved multi-channel Gabor filter-based human face recognition method according to an embodiment of the present invention.
  • FIG. 4 is a diagram illustrating a Yale face database used in an improved multi-channel Gabor filter-based human face recognition method according to an embodiment of the present invention.
  • FIG. 5 illustrates the effect of image block size and bin on CS-LBP for explaining an improved multi-channel Gabor filter-based human face recognition method according to an embodiment of the present invention
  • FIG. 6 illustrates image block size and storage for LBP. A graph showing the effect of.
  • FIG. 7 illustrates an ORL face database used in an improved multi-channel Gabor filter-based human face recognition method according to an embodiment of the present invention.
  • FIG. 7 illustrates a FERET face database used in an improved multi-channel Gabor filter-based human face recognition method according to an embodiment of the present invention.
  • the component when one component 'transmits' data or a signal to another component, the component may directly transmit the data or signal to another component, and through at least one other component. This means that data or signals can be transmitted to other components.
  • FIG. 1 is a diagram illustrating a feature extraction algorithm module 100 in which an improved multi-channel Gabor filter-based human face recognition method is performed according to an embodiment of the present invention.
  • a "feature extraction algorithm module 100" based on a combination of Center-Symmetric Local Binary Patterns (CS-LBP) and Gabor Wavelet is performed.
  • CS-LBP Center-Symmetric Local Binary Patterns
  • Feature extraction algorithm module 100 is a Gabor filter using Gabor Wavelet transform means 110 and a CS-LBP operator to perform Gabor wavelet transform combined with CS-LBP face image feature extraction algorithm.
  • Image texture extracting function means 120 is a Gabor filter using Gabor Wavelet transform means 110 and a CS-LBP operator to perform Gabor wavelet transform combined with CS-LBP face image feature extraction algorithm.
  • the Gabor Wavelet transform means 110 extracts a multi-scale image using optional attributes such as good spatial locality, spatial frequency and image.
  • the Gabor filter image texture extraction function means 120 using the CS-LBP operator improves the robustness of the external environment change. This is because changes in the external environment, such as the direction of the region and other important features, lighting, expression, posture and shade, are robust.
  • the algorithm based on the combination of Center-Symmetric Local Binary Patterns (CS-LBP) and Gabor Wavelet of the present invention is an algorithm in space and time. In addition to reducing the overhead of, it can represent a significant recognition rate.
  • the Gabor filter image texture extracting function unit 120 using the CS-LBP operator may analyze the gray scale change of the scale and the image in all directions by a group of various scales having different filter directions.
  • the Gabor filter image texture extraction function means 120 using the CS-LBP operator has good time-frequency localization and multiple resolution characteristics, and has the ability to extract local nuances of an image.
  • the Gabor filter image texture extraction function means 120 using the CS-LBP operator ensures robustness in lighting variations, image rotations and deformations.
  • the Gabor wavelet kernel function used by the Gabor filter image texture extraction function means 120 using the CS-LBP operator is expressed by Equation 1 below.
  • ⁇ and v correspond to the direction and size of the filter, respectively, adjust the ⁇ and v to select the direction and dimension of the filter.
  • v ⁇ ⁇ 0, 1, 2, 3, 4 ⁇ , ⁇ ⁇ ⁇ 0, 1, 2, 3, 4, 5, 6, 7 ⁇ , , to be.
  • FIG. 2 shows images of Gabor amplitude spectra used in an improved multi-channel Gabor filter-based human face recognition method according to an embodiment of the present invention.
  • the feature extraction algorithm module 100 combines Gabor filter images according to the following two methods to reduce the size of the Gabor function, and uses the multi-channel Gabor function to reduce the number of Gabor filter images and maintain the images. Extract the multiscale image from the original image.
  • Gabor filter images of the two combination methods are as follows.
  • the feature extraction algorithm module 100 adjusts each scale in the direction of the Gabor filter image overlay in various scales of the multi-channel Gabor function image to obtain a multi-frequency Gabor channel (MFGC).
  • MFGC multi-frequency Gabor channel
  • the feature extraction algorithm module 100 obtains a multi-directional Gabor channel (MOGC) in each direction in the scaled image overlay in the other direction of the multi-channel Gabor feature image Gabor filter.
  • MOGC multi-directional Gabor channel
  • FIG. 3 is a diagram illustrating an improved multi-channel Gabor filter-based human face recognition method according to an embodiment of the present invention.
  • the improved multi-channel Gabor filter-based human face recognition method is performed by the feature extraction algorithm module 100 of FIG. 1, and the feature extraction algorithm module 100 includes a multi-channel Gabor filter and a CS-LBP. It may be performed by combining MOGC and CS-LBP based on facial image feature extraction algorithm.
  • the feature extraction algorithm module 100 may include image Gabor wavelet transform, superposition of Gabor amplitude spectra, superposition feature CS-LBP combination after MOGC extracted image coding, block and statistical histogram, for face feature extraction and face feature reduction. Cascade histogram style feature vector sequencing can be performed.
  • Output is an image of a cascading particle histogram feature vector representation of the statistics.
  • the feature extraction algorithm module 100 Acquire feature images, , to be.
  • step 1-1 Heirloom transformed to obtain a heirloom amplitude spectrum, and for superposed n amplitude spectra by superposition in steps 1-2, And obtain an image for each CS-LBP code in steps 1-3. Acquire.
  • the feature extraction algorithm module 100 In the same way, separate multiple overlapping sub-images.
  • the feature extraction algorithm module 100 Create a histogram of all sub-images of, and extract the cascading histogram feature vector sequence.
  • the feature extraction algorithm module 100 Extract the corresponding eigenvectors of the cascade sequence.
  • the feature extraction algorithm module 100 extracts a face image feature vector after feature extraction in the first to fourth steps described above.
  • the feature extraction algorithm module 100 in the face recognition step of Equation 2 You can use the distance function to calculate test samples and learn sample similarities.
  • T denotes a one-dimensional histogram feature vector of the training sample
  • S denotes a test sample one-dimensional histogram eigenvector
  • P denotes the number of sub-images
  • Q denotes the number of sub-image histogram bins.
  • r and i are exponents for P and Q, respectively.
  • the CS-LBP operator and the LBP operator for parameter selection are used. Blocking the size of sub-images affects recognition performance. If the block is too large, the extreme block size is the original image size and cannot reflect the benefits of local areas of image analysis.
  • Yale's face database has 11 images, including 15 people, and a total of 165 positive face images, including facial expressions and contrast changes.
  • a portion of Yale is some sample image database of a person at Yale, such as FIG.
  • FIGS. 5 and 6 show the effect of image block size and storage on LBP.
  • the recognition rate is all 8 ⁇ 8 as the image block size, and when the bins are 16 and 8, the two recognition rate curves are very close, so we choose the CS-LBP algorithm.
  • the parameter image block size is 8 * 8 and the number of bins is 8.
  • the CS-LBP algorithm based on the MFGC and MOGC extracting the feature dimension is 1/32 of the LBP, and the CS-LBP algorithm extracts the feature point dimension much lower than the LBP algorithm.
  • CS-LBP has a greater advantage in the time required for training samples
  • the CS-LBP algorithm is still superior in terms of test time, and takes half of LBP.
  • the longest Gabor + LBP algorithm requires 7.0 seconds and the MOGC + CS-LBP algorithm requires 0.27 seconds. Therefore, CSP-LBP extracts feature point dimensions over LBP, has more advantages in the time required for training and test samples, and can perform image feature point extraction more effectively.
  • the test plan randomly selects 3, 4, 5, 6, 7 images as training samples, the rest as test samples, and repeats 10 tests.
  • Table 2 shows the results of the algorithm's recognition rate on Yale's face database according to five kinds of test comparison algorithms.
  • the CS-LBP algorithm achieved a significant recognition rate compared to the LBP algorithm, and the recognition rate increased by almost 1% for 4 and 6 in the test sample.
  • Gabor + LBP algorithm, MOGC + CS-LBP algorithm achieved a significant recognition rate compared to the LBP algorithm, and the recognition rate improved about 2% compared to the CS-LBP algorithm.
  • the ORL face database contains 40 people, all have 10 images, a total of 400 face images.
  • FIG. 7 shows ten sample images of a person in an ORL.
  • Table 3 shows the test results for the five types of algorithms in the iterative test. That is, Table 3 shows the recognition rate results of the five algorithms for the ORL.
  • Training sample number 3 4 5 6 LBP 86.93 91.08 94.10 96.00 CS-LBP 88.09 91.70 94.40 96.00 Gabor + LBP 89.78 93.32 95.54 96.89 MFGC + CS-LBP 88.36 91.79 93.50 94.69 MOGC + CS-LBP 90.32 93.54 95.65 97.00
  • Table 4 shows the test comparison results for five different algorithms according to the iterative test and the 10-fold average test. That is, Table 4 shows the recognition rate results of the five algorithms in FERET.
  • Training sample number 2 3 4 LBP 82.79 88.86 92.33 CS-LBP 82.21 89.00 91.79 Gabor + LBP 86.70 91.33 92.89 MFGC + CS-LBP 85.86 90.03 92.00 MOGC + CS-LBP 87.37 91.42 93.00
  • the MOGC + CS-LBP algorithm has a low level of feature extraction and a significant recognition rate compared to the Gabor + LBP algorithm. That is, the MOGC + CS-LBP algorithm has the best recognition rate and the accuracy of the recognition rate is improved.
  • the invention can also be embodied as computer readable code on a computer readable recording medium.
  • Computer-readable recording media include all kinds of recording devices that store data that can be read by a computer system.
  • Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disks, optical data storage devices, and the like, which are also implemented in the form of carrier waves (eg, transmission over the Internet). It also includes.
  • the computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.
  • functional programs, codes and code segments for implementing the present invention can be easily inferred by programmers in the art to which the present invention belongs.
  • the present invention provides an improved multi-channel Gabor for human face recognition combining Gabor filter and Center-symmetric local binary patterns (CS-LBP) to reduce the noise's robust feature point extraction and the high dimensionality of the extracted facial feature points.
  • CS-LBP Center-symmetric local binary patterns
  • the improved multi-channel Gabor filter-based human face recognition method reduces the dimension of the feature image by combining the Gabor feature images in different directions and scales, and based on CS-LBP based low dimension from the feature image. Provides the effect of extracting facial feature points.
  • the improved multi-channel Gabor filter-based human face recognition method is characterized by a feature dimension, a storage space, and a combination of the Gabor filter and the CS-LBP method, compared to the conventional Gabor filter and LBP approach. It reduces the computation time and at the same time provides the effect of high accuracy of face recognition.

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Abstract

La présente invention concerne un procédé de reconnaissance de visage humain basé sur un filtre de Gabor multicanal amélioré. La présente invention comprend : une première étape d'acquisition d'une image caractéristique lorsqu'une entrée est d'images faciales de N personnes (N est un entier naturel de deux ou plus) et une sortie est une image d'une expression vectorielle de caractéristiques d'histogramme de particules échelonnées de statistiques; une deuxième étape consistant à diviser les images de visage respectives en une pluralité de sous-images se chevauchant de la même manière; et une troisième étape consistant à générer un histogramme de toutes les sous-images de façon à extraire une séquence de vecteurs de caractéristiques d'histogramme en escalier. Par conséquent, le filtre de Gabor et les motifs binaires locaux à symétrie centrale (CS-LBP) sont combinés, ce qui permet d'extraire des points caractéristiques, qui sont robustes contre le bruit, et de fournir un effet permettant de réduire une dimensionnalité élevée du point caractéristique de visage extrait.
PCT/KR2017/001886 2017-02-15 2017-02-21 Procédé de reconnaissance de visage humain basé sur un filtre de gabor multicanal amélioré Ceased WO2018151357A1 (fr)

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