Zhang et al., 2019 - Google Patents
Deep cascade model-based face recognition: When deep-layered learning meets small dataZhang et al., 2019
- Document ID
- 2504271839708801640
- Author
- Zhang L
- Liu J
- Zhang B
- Zhang D
- Zhu C
- Publication year
- Publication venue
- IEEE Transactions on Image Processing
External Links
Snippet
Sparse representation based classification (SRC), nuclear-norm matrix regression (NMR), and deep learning (DL) have achieved a great success in face recognition (FR). However, there still exist some intrinsic limitations among them. SRC and NMR based coding methods …
- 239000011159 matrix material 0 abstract description 29
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
- G06K9/6217—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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- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
- G06K9/6203—Shifting or otherwise transforming the patterns to accommodate for positional errors
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- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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