WO2006014097A1 - Procede de determination automatique de la conformite d'une image normalisee numerisee electronique avec une image d'un visage - Google Patents

Procede de determination automatique de la conformite d'une image normalisee numerisee electronique avec une image d'un visage Download PDF

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Publication number
WO2006014097A1
WO2006014097A1 PCT/KZ2005/000005 KZ2005000005W WO2006014097A1 WO 2006014097 A1 WO2006014097 A1 WO 2006014097A1 KZ 2005000005 W KZ2005000005 W KZ 2005000005W WO 2006014097 A1 WO2006014097 A1 WO 2006014097A1
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WO
WIPO (PCT)
Prior art keywords
image
values
derivatives
face
horizontal
Prior art date
Application number
PCT/KZ2005/000005
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English (en)
Russian (ru)
Inventor
Daulet Kulenov
Alexandr Lobanov
Alexey Tikhonov
Original Assignee
Daulet Kulenov
Alexandr Lobanov
Alexey Tikhonov
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Daulet Kulenov, Alexandr Lobanov, Alexey Tikhonov filed Critical Daulet Kulenov
Publication of WO2006014097A1 publication Critical patent/WO2006014097A1/fr

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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
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships

Definitions

  • the invention relates to methods for pattern recognition by highlighting certain characteristics of an image and can be used to automatically determine whether an electronic digitized normalized image matches a face image by comparing its characteristics with the reference ones obtained and modified by the adaptive method during training.
  • Quick and reliable determination of the correspondence of an electronic image to a normalized face image is one of the most important tasks of preliminary image processing and serves as a necessary basis for the successful implementation of further transformations in comparison and identification of a person.
  • the closest to the proposed method is a method for detecting a face and its elements using deformable standards (Goldstep AJ, Narmop LD, Lesk AB, "Ideptufopf Numap Faces", - Roc. IEEE, Mau 1971, vol 59), in which the visual image is transformed into digital signals corresponding to the normalized image process the received signals, while digital signals are presented in the form of a numerical array of values corresponding to the visual image, which after corresponding normalization is compared in a suitable metric with a standard representing deformed variations of the face or its individual elements.
  • this method is also inferior in terms of detection reliability to methods for detecting a face and its elements based on PCA and also requires significant time costs.
  • all known methods for detecting faces in an image require significant computational and time consuming, are quite complex and time-consuming.
  • the task to which the invention is directed is the creation of a simple and effective method for automatically determining the correspondence of an electronic digitized normalized image to a face image.
  • the technical result of the invention is to reduce the processing time of visual images, and increase the reliability of personal identification in real time.
  • This technical result is achieved by the fact that at the first stage the visual image is converted into digital signals corresponding to the normalized image, and these signals are presented in the form of a numerical matrix corresponding to the visual image, after which, according to the invention, the learning process is carried out over an extensive sample (at least 10,000) normalized images of faces, which includes: the calculation of vertical and horizontal derivatives for which the image is processed using a filter in high frequencies (HPF) horizontally and vertically, determining the distribution function of the values of the matrices of derivatives at each point and determining the minimum and maximum threshold values of the interval in which most of the values of the corresponding derivatives are located.
  • HPF high frequencies
  • the threshold values of the derivatives at each point in the image are determined, which are then used in the classification.
  • the classification problem is solved, that is, the determination of the presence or absence of normalized image of the face on the incoming image for identification.
  • vertical and horizontal derivatives are successively calculated and their values are checked for threshold values obtained during training, as a result of which a certain value is assigned to each image point. If the sum of such values exceeds a certain threshold determined empirically, a conclusion follows about the presence of a face image in the current image, otherwise, the opposite conclusion is made.
  • This method of image classification provides a significant increase in processing speed with high face detection efficiency, which allows the proposed method to be used in real-time identification and access control systems.
  • Figure l shows typical examples of complete (left) and normalized (right) images from a sample of faces.
  • Figure 2 shows the results of applying a high-pass filter (HPF) horizontally and vertically for the image of Fig.l.
  • HPF high-pass filter
  • Figure 4 shows a graph of the probability of an incorrect acceptance of an object (Falls Assert Rat-FAR) on the threshold level
  • Figure 5 is a graph of the dependence of the probability of skipping an object (Fall Asset Rat-FRR) on the threshold level.
  • the implementation of the method is carried out in two stages.
  • the visual image is converted into digital signals corresponding to the normalized image, the received signals are processed, and the digital signals are presented in the form of a numerical array of values corresponding to the visual image, after which the learning process is carried out using an extensive selection of normalized entities representing the variability of many faces.
  • Figure l shows an example of a complete image from a sample and a normalized image of a face.
  • the learning process consists in determining the maximum value at each point of the processed HPF images from a sample of individuals.
  • FIG. 2 shows the results of applying a horizontal and vertical high-pass filter for the image of FIG.
  • Fig. 3 shows masks obtained in the learning process.
  • the incoming image is directly classified, which is carried out as follows.
  • the image is processed by the HPF horizontally and vertically.
  • the values at certain points of the image corresponding to the maximum values of the corresponding mask and exceeding a certain threshold determined experimentally are summarized.
  • the values at the image points corresponding to the minimum values of the mask, and whose values are less than a certain minimum threshold are summed.
  • two coefficients corresponding to the image processed by the horizontal high-pass filter and two image coefficients processed by the vertical high-pass filter are obtained.
  • the end result is determined as the weighted sum of all four coefficients.
  • the threshold was determined empirically, when exceeded, the image belongs to the class of persons, otherwise the opposite conclusion is made.
  • FIG. Figures 4 and 5 show graphs of the statistical characteristics of the method for the probability of incorrect acceptance of an object (FAR) and the probability of skipping an object (Falls Relay Ratt - FRR) on the threshold level. These characteristics were obtained after testing the method for an extensive sample of images.

Abstract

La présente invention concerne des procédés de reconnaissance de figures par sélection de certaines caractéristiques d'une image et peut être utilisée pour déterminer automatiquement la conformité d'une image normalisée numérisée électronique avec une image d'un visage au moyen de la comparaison des caractéristiques de l'image avec des caractéristiques de référence pouvant être obtenues et modifiées au moyen d'un procédé adaptatif au cours de l'apprentissage. Cette invention permet de réduire le temps de traitement d'images visuelles et d'accroître le degré de certitude de l'identification d'un individu en temps réel. Le procédé de cette invention permettant de déterminer automatiquement la conformité d'une image normalisée numérisée électronique avec une image d'un visage consiste à convertir une image visuelle en un tableau bidimensionnel numérique d'une taille déterminée, à appliquer un processus d'apprentissage sur la base d'un échantillon élargi d'images normalisées de visages (n'excédant pas 10000), lequel processus consiste à effectuer un traitement d'images au moyen d'un filtre haute fréquence dans des directions horizontale et verticale afin de calculer des dérivés verticales et horizontales, à déterminer la fonction d'une répartition de valeurs de grandeur de matrices de dérivés au niveau de chaque point et à déterminer des valeurs seuils maximale et minimale d'une gamme dans laquelle se trouve la majorité des grandeurs des dérivés correspondantes. Le procédé consiste également à classer l'image identifiable au moyen du traitement de cette image à l'aide d'un filtre haute fréquence dans des directions horizontale et verticale afin de calculer les dérivés verticales et horizontales de l'image identifiable, à vérifier la conformité des valeurs des dérivés avec les valeurs seuils pouvant être obtenues au cours de l'apprentissage, à attribuer une valeur déterminée à chaque point et, si la somme de ces valeurs est supérieure à un seuil donné déterminé par l'expérience, à conclure que l'image du visage est présente sur une image courante et, dans le cas contraire, à tirer la conclusion opposée.
PCT/KZ2005/000005 2004-08-04 2005-07-11 Procede de determination automatique de la conformite d'une image normalisee numerisee electronique avec une image d'un visage WO2006014097A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KZ2004/1130.1 2004-08-04
KZ20041130 2004-08-04

Publications (1)

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WO2006014097A1 true WO2006014097A1 (fr) 2006-02-09

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108122207A (zh) * 2016-11-30 2018-06-05 展讯通信（上海）有限公司 图像分频方法、装置及电子设备
CN110399787A (zh) * 2019-06-10 2019-11-01 万翼科技有限公司 一种工程图纸的管理系统及方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SU1048492A1 (ru) * 1981-11-13 1983-10-15 Предприятие П/Я Р-6681 Способ выделени признаков при распознавании изображени
JPH1185988A (ja) * 1997-09-04 1999-03-30 Fujitsu Ltd 顔画像認識システム
RU2175148C1 (ru) * 2000-04-04 2001-10-20 Свириденко Андрей Владимирович Способ идентификации человека
WO2003030089A1 (fr) * 2001-09-28 2003-04-10 Koninklijke Philips Electronics N.V. Systeme et methode de reconnaissance faciale par demi-faces

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SU1048492A1 (ru) * 1981-11-13 1983-10-15 Предприятие П/Я Р-6681 Способ выделени признаков при распознавании изображени
JPH1185988A (ja) * 1997-09-04 1999-03-30 Fujitsu Ltd 顔画像認識システム
RU2175148C1 (ru) * 2000-04-04 2001-10-20 Свириденко Андрей Владимирович Способ идентификации человека
WO2003030089A1 (fr) * 2001-09-28 2003-04-10 Koninklijke Philips Electronics N.V. Systeme et methode de reconnaissance faciale par demi-faces

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108122207A (zh) * 2016-11-30 2018-06-05 展讯通信（上海）有限公司 图像分频方法、装置及电子设备
CN110399787A (zh) * 2019-06-10 2019-11-01 万翼科技有限公司 一种工程图纸的管理系统及方法

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