WO2008015586A3 - Face recognition with combined pca-based datasets - Google Patents
Face recognition with combined pca-based datasets Download PDFInfo
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- WO2008015586A3 WO2008015586A3 PCT/IB2007/003985 IB2007003985W WO2008015586A3 WO 2008015586 A3 WO2008015586 A3 WO 2008015586A3 IB 2007003985 W IB2007003985 W IB 2007003985W WO 2008015586 A3 WO2008015586 A3 WO 2008015586A3
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- 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
- G06V40/169—Holistic features and representations, i.e. based on the facial image taken as a whole
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
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- 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
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
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- 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
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
- G06V10/806—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level of extracted features
Abstract
A face recognition method for working with two or more collections of facial images is provided. A representation framework is determined for a first collection of facial images including at least principle component analysis (PCA) features. A representation of said first collection is stored using the representation framework. A modified representation framework is determined based on statistical properties of original facial image samples of a second collection of facial images and the stored representation of the first collection. The first and second collections are combined without using original facial image samples. A representation of the combined image collection (super-collection) is stored using the modified representation framework. A representation of a current facial image, determined in terms of the modified representation framework, is compared with one or more representations of facial images of the combined collection. Based on the comparing, it is determined which, if any, of the facial images within the combined collection matches the current facial image.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
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EP07849030A EP2050043A2 (en) | 2006-08-02 | 2007-08-02 | Face recognition with combined pca-based datasets |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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US82116506P | 2006-08-02 | 2006-08-02 | |
US60/821,165 | 2006-08-02 |
Publications (2)
Publication Number | Publication Date |
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WO2008015586A2 WO2008015586A2 (en) | 2008-02-07 |
WO2008015586A3 true WO2008015586A3 (en) | 2008-08-07 |
Family
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Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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PCT/IB2007/003985 WO2008015586A2 (en) | 2006-08-02 | 2007-08-02 | Face recognition with combined pca-based datasets |
Country Status (3)
Country | Link |
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US (3) | US7515740B2 (en) |
EP (1) | EP2050043A2 (en) |
WO (1) | WO2008015586A2 (en) |
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Also Published As
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US20090238410A1 (en) | 2009-09-24 |
US20080031498A1 (en) | 2008-02-07 |
US8897503B2 (en) | 2014-11-25 |
WO2008015586A2 (en) | 2008-02-07 |
EP2050043A2 (en) | 2009-04-22 |
US7515740B2 (en) | 2009-04-07 |
US8050466B2 (en) | 2011-11-01 |
US20120230554A1 (en) | 2012-09-13 |
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