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Filippidou et al., 2020 - Google Patents

Single sample face recognition using convolutional neural networks for automated attendance systems

Filippidou et al., 2020

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Document ID
1652171018376087248
Author
Filippidou F
Papakostas G
Publication year
Publication venue
2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS)

External Links

Snippet

Convolutional Neural Networks (CNNs) have been developed as powerful models for image recognition problems requiring large-scale labeled training data. However, estimating millions parameters of deep CNNs requires a huge amount of labeled samples, restricting …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

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    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6255—Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries, e.g. user dictionaries
    • G—PHYSICS
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00288—Classification, e.g. identification
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • G—PHYSICS
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    • G—PHYSICS
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    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
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    • G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of the preceding main groups, e.g. automatic card files incorporating conveying and reading operations

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