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WO2006057475A1 - Appareil et procede de detection et d'authentification de visage - Google Patents

Appareil et procede de detection et d'authentification de visage Download PDF

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Publication number
WO2006057475A1
WO2006057475A1 PCT/KR2004/003480 KR2004003480W WO2006057475A1 WO 2006057475 A1 WO2006057475 A1 WO 2006057475A1 KR 2004003480 W KR2004003480 W KR 2004003480W WO 2006057475 A1 WO2006057475 A1 WO 2006057475A1
Authority
WO
WIPO (PCT)
Prior art keywords
lace
data
image
detection
authentication
Prior art date
Application number
PCT/KR2004/003480
Other languages
English (en)
Inventor
Kicheon Hong
Kwang-Seok Hong
Ji-Hong Min
Original Assignee
Kicheon Hong
Kwang-Seok Hong
Ji-Hong Min
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
Priority claimed from KR1020040096171A external-priority patent/KR100702225B1/ko
Priority claimed from KR1020040096172A external-priority patent/KR100702226B1/ko
Application filed by Kicheon Hong, Kwang-Seok Hong, Ji-Hong Min filed Critical Kicheon Hong
Publication of WO2006057475A1 publication Critical patent/WO2006057475A1/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 AdaBoost learning algorithm is used for enhancing lace recognition rates.
  • the AdaBoost learning algorithm which is simple but efficient compared to other boost algorithms, is used for more precisely differentiating a lacial area from a non ⁇ acial area and boosting the probability of an area of an image determined to be a lacial area.
  • locations of prototypes of Haar-like features can render more detailed lacial characteristics especially at a higher stage of classification.
  • Haar-like features obtained using the AdaBoost learning algorithm are classified in stages, as shown in FIG. 2. Referring to FIG.
  • an area of an image of one frame is determined as a lacial area using a group of trained lace images 300 while gradually decreasing the size of the image in a pyramid manner.
  • a plurality of candidate areas for the lacial area of the image are generated in the process of recovering the size of the image, and an average of the candidate areas is output.
  • Facial areas of trained lace images are detected and then differentiated from non ⁇ acial areas of the trained lace images using lace classifiers. Thereafter, lace authentication is performed by comparing the detected fecial area with the trained lace images using an HMM.
  • a feature extraction unit 520 extracts features O , O , ..., O from the
  • Table 1 shows trained location information and critical values of prototypes of
  • FIG. 7 is a diagram illustrating lace authentication results obtained using the lace detection and authentication method according to an exemplary embodiment of the present invention.
  • 48 lace images of 8 people (6 lace images per person) and a trained database of 24) lace images of 4) people (6 lace images per person) were used in experiments for testing the lace detection and authentication apparatus according to the exemplary embodiment of the present invention.
  • the size of the forehead in lace images of people who ⁇ iled to be authenticated is larger than in lace images of people who were successfiilly verified, as shown in FlG. 7. Because of a larger forehead, the eyes in the lace images of those who f ⁇ iled to be authenticated deviate from expected locations when segmenting the corresponding lace images in a 2D HMM-based training process.
  • FIG. 9 is a diagram illustrating another example of the lace detection and au- thentication method according to the exemplary embodiment of the present invention that uses a WPS 80.
  • the WPS 83 automatically extracts &ce data from an image to be authenticated using a lace detection algorithm stored therein and wirelessly transmits the extracted lace data using a JPEG codec to a server 90.
  • the server 90 stores a database of lace images and a lace authentication algorithm.
  • the server 90 includes a lace authentication unit, which authenticates the image to be authenticated by comparing the extracted lace data with lace data stored therein.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)
  • Collating Specific Patterns (AREA)

Abstract

L'invention porte sur un appareil et un procédé de détection et d'authentification de visage, permettant de décider s'il faut authentifier des données de visage détectées à partir d'une image de visage entrée au moyen d'algorithmes de détection par comparaison avec les données de visage détectées avec les données de visage préalablement stockées. Cet appareil et ce procédé de détection et d'authentification de visage peuvent détecter des données de visage à partir d'une image mobile et peuvent authentifier les données de visage détectées au moyen d'une station personnelle portable (WPS) et un serveur ou au moyen seulement de la station personnelle portable, ce qui permet de fournir des solutions efficaces permettant d'identifier et d'authentifier des individus, et de leur fournir une fonction d'ouverture de session, et de tenir des conférences vidéo.
PCT/KR2004/003480 2004-11-23 2004-12-28 Appareil et procede de detection et d'authentification de visage WO2006057475A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR1020040096171A KR100702225B1 (ko) 2004-11-23 2004-11-23 얼굴 검출 및 인증 장치 및 그 방법
KR1020040096172A KR100702226B1 (ko) 2004-11-23 2004-11-23 얼굴 검출 및 인증 장치 및 그 방법
KR10-2004-0096172 2004-11-23
KR10-2004-0096171 2004-11-23

Publications (1)

Publication Number Publication Date
WO2006057475A1 true WO2006057475A1 (fr) 2006-06-01

Family

ID=36498191

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2004/003480 WO2006057475A1 (fr) 2004-11-23 2004-12-28 Appareil et procede de detection et d'authentification de visage

Country Status (1)

Country Link
WO (1) WO2006057475A1 (fr)

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8643691B2 (en) 2008-05-12 2014-02-04 Microsoft Corporation Gaze accurate video conferencing
CN104298969A (zh) * 2014-09-25 2015-01-21 电子科技大学 基于颜色与haar特征融合的人群规模统计方法
CN104978550A (zh) * 2014-04-08 2015-10-14 上海骏聿数码科技有限公司 基于大规模人脸数据库的人脸识别方法及系统
CN105260744A (zh) * 2015-10-08 2016-01-20 北京航空航天大学 一种货运列车钩尾扁销部位故障的自动在线诊断方法及系统
CN106778445A (zh) * 2015-11-20 2017-05-31 沈阳新松机器人自动化股份有限公司 基于人脸检测的服务机器人视觉引领方法
CN106971193A (zh) * 2016-11-23 2017-07-21 南京理工大学 基于结构型Haar和Adaboost的目标检测方法
CN107315993A (zh) * 2017-05-10 2017-11-03 苏州天平先进数字科技有限公司 一种基于人脸识别的门镜系统及其人脸识别方法
CN109711403A (zh) * 2018-12-18 2019-05-03 电子科技大学 一种基于Haar特征和Adaboost的白带中霉菌检测方法
US10678903B2 (en) 2016-05-02 2020-06-09 Hewlett-Packard Development Company, L.P. Authentication using sequence of images
US11875268B2 (en) 2014-09-22 2024-01-16 Samsung Electronics Co., Ltd. Object recognition with reduced neural network weight precision

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6088451A (en) * 1996-06-28 2000-07-11 Mci Communications Corporation Security system and method for network element access
US20030142854A1 (en) * 2002-01-30 2003-07-31 Samsung Electronics Co., Ltd. Apparatus and method for providing security in a base or mobile station by using detection of face information
US6633655B1 (en) * 1998-09-05 2003-10-14 Sharp Kabushiki Kaisha Method of and apparatus for detecting a human face and observer tracking display

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6088451A (en) * 1996-06-28 2000-07-11 Mci Communications Corporation Security system and method for network element access
US6633655B1 (en) * 1998-09-05 2003-10-14 Sharp Kabushiki Kaisha Method of and apparatus for detecting a human face and observer tracking display
US20030142854A1 (en) * 2002-01-30 2003-07-31 Samsung Electronics Co., Ltd. Apparatus and method for providing security in a base or mobile station by using detection of face information

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8643691B2 (en) 2008-05-12 2014-02-04 Microsoft Corporation Gaze accurate video conferencing
CN104978550A (zh) * 2014-04-08 2015-10-14 上海骏聿数码科技有限公司 基于大规模人脸数据库的人脸识别方法及系统
CN104978550B (zh) * 2014-04-08 2018-09-18 上海骏聿数码科技有限公司 基于大规模人脸数据库的人脸识别方法及系统
US11875268B2 (en) 2014-09-22 2024-01-16 Samsung Electronics Co., Ltd. Object recognition with reduced neural network weight precision
CN104298969A (zh) * 2014-09-25 2015-01-21 电子科技大学 基于颜色与haar特征融合的人群规模统计方法
CN105260744A (zh) * 2015-10-08 2016-01-20 北京航空航天大学 一种货运列车钩尾扁销部位故障的自动在线诊断方法及系统
CN105260744B (zh) * 2015-10-08 2018-08-14 北京航空航天大学 一种货运列车钩尾扁销部位故障的自动在线诊断方法及系统
CN106778445A (zh) * 2015-11-20 2017-05-31 沈阳新松机器人自动化股份有限公司 基于人脸检测的服务机器人视觉引领方法
US10678903B2 (en) 2016-05-02 2020-06-09 Hewlett-Packard Development Company, L.P. Authentication using sequence of images
CN106971193A (zh) * 2016-11-23 2017-07-21 南京理工大学 基于结构型Haar和Adaboost的目标检测方法
CN107315993A (zh) * 2017-05-10 2017-11-03 苏州天平先进数字科技有限公司 一种基于人脸识别的门镜系统及其人脸识别方法
CN109711403A (zh) * 2018-12-18 2019-05-03 电子科技大学 一种基于Haar特征和Adaboost的白带中霉菌检测方法

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