Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313532597> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W4313532597 endingPage "894" @default.
- W4313532597 startingPage "881" @default.
- W4313532597 abstract "Face recognition has been widely studied in the artificial intelligence field. Especially, with the increasing availability of machine learning algorithms such as deep learning, most existing research uses massive datasets to facilitate better recognition performance. However, it is time-consuming and labor-intensive to collect and annotate a large face dataset. On the contrary, with a small dataset, some of the machine learning algorithms may fail to perform the recognition task. In order to study the performances of different machine learning algorithms, this work compares the recognition performances of seven widely used machine learning algorithms on three different face datasets. With shallow data, the experimental results reveal that the best recognition accuracy on different datasets can be achieved by different algorithms. Specifically, the best performance of 66.41% on the Extended Yale B dataset is achieved by the logistic regression algorithm, the LDA algorithm achieves the best performance of 97.5% on the Olivetti face dataset, while the random forest algorithm obtains the best accuracy of 10.31% on the LFW face dataset, with five shallow training samples. The obtained results can be used to select appropriate ML algorithms to recognize faces with shallow data." @default.
- W4313532597 created "2023-01-06" @default.
- W4313532597 creator A5012677279 @default.
- W4313532597 creator A5028358992 @default.
- W4313532597 creator A5035406376 @default.
- W4313532597 creator A5061821393 @default.
- W4313532597 date "2023-01-01" @default.
- W4313532597 modified "2023-10-16" @default.
- W4313532597 title "Machine Learning for Face Recognition in Shallow Data" @default.
- W4313532597 cites W1519268598 @default.
- W4313532597 cites W2040870580 @default.
- W4313532597 cites W2051434435 @default.
- W4313532597 cites W2060117429 @default.
- W4313532597 cites W2068141066 @default.
- W4313532597 cites W2125874614 @default.
- W4313532597 cites W2216946510 @default.
- W4313532597 cites W2769892801 @default.
- W4313532597 cites W2776146695 @default.
- W4313532597 cites W2790350006 @default.
- W4313532597 cites W2897905679 @default.
- W4313532597 cites W2901772140 @default.
- W4313532597 cites W2932296145 @default.
- W4313532597 cites W2963156201 @default.
- W4313532597 cites W2996597849 @default.
- W4313532597 cites W3046220160 @default.
- W4313532597 cites W3094843939 @default.
- W4313532597 cites W3173309192 @default.
- W4313532597 cites W3196025635 @default.
- W4313532597 cites W3204217618 @default.
- W4313532597 cites W4214808100 @default.
- W4313532597 doi "https://doi.org/10.1007/978-3-031-21438-7_74" @default.
- W4313532597 hasPublicationYear "2023" @default.
- W4313532597 type Work @default.
- W4313532597 citedByCount "1" @default.
- W4313532597 countsByYear W43135325972023 @default.
- W4313532597 crossrefType "book-chapter" @default.
- W4313532597 hasAuthorship W4313532597A5012677279 @default.
- W4313532597 hasAuthorship W4313532597A5028358992 @default.
- W4313532597 hasAuthorship W4313532597A5035406376 @default.
- W4313532597 hasAuthorship W4313532597A5061821393 @default.
- W4313532597 hasConcept C11413529 @default.
- W4313532597 hasConcept C119857082 @default.
- W4313532597 hasConcept C127413603 @default.
- W4313532597 hasConcept C144024400 @default.
- W4313532597 hasConcept C153180895 @default.
- W4313532597 hasConcept C154945302 @default.
- W4313532597 hasConcept C169258074 @default.
- W4313532597 hasConcept C201995342 @default.
- W4313532597 hasConcept C202444582 @default.
- W4313532597 hasConcept C2779304628 @default.
- W4313532597 hasConcept C2780451532 @default.
- W4313532597 hasConcept C31510193 @default.
- W4313532597 hasConcept C33923547 @default.
- W4313532597 hasConcept C36289849 @default.
- W4313532597 hasConcept C41008148 @default.
- W4313532597 hasConcept C9652623 @default.
- W4313532597 hasConceptScore W4313532597C11413529 @default.
- W4313532597 hasConceptScore W4313532597C119857082 @default.
- W4313532597 hasConceptScore W4313532597C127413603 @default.
- W4313532597 hasConceptScore W4313532597C144024400 @default.
- W4313532597 hasConceptScore W4313532597C153180895 @default.
- W4313532597 hasConceptScore W4313532597C154945302 @default.
- W4313532597 hasConceptScore W4313532597C169258074 @default.
- W4313532597 hasConceptScore W4313532597C201995342 @default.
- W4313532597 hasConceptScore W4313532597C202444582 @default.
- W4313532597 hasConceptScore W4313532597C2779304628 @default.
- W4313532597 hasConceptScore W4313532597C2780451532 @default.
- W4313532597 hasConceptScore W4313532597C31510193 @default.
- W4313532597 hasConceptScore W4313532597C33923547 @default.
- W4313532597 hasConceptScore W4313532597C36289849 @default.
- W4313532597 hasConceptScore W4313532597C41008148 @default.
- W4313532597 hasConceptScore W4313532597C9652623 @default.
- W4313532597 hasLocation W43135325971 @default.
- W4313532597 hasOpenAccess W4313532597 @default.
- W4313532597 hasPrimaryLocation W43135325971 @default.
- W4313532597 hasRelatedWork W1775397219 @default.
- W4313532597 hasRelatedWork W2001423728 @default.
- W4313532597 hasRelatedWork W2044260117 @default.
- W4313532597 hasRelatedWork W2134472250 @default.
- W4313532597 hasRelatedWork W2159857630 @default.
- W4313532597 hasRelatedWork W2347601237 @default.
- W4313532597 hasRelatedWork W2897995864 @default.
- W4313532597 hasRelatedWork W2911455822 @default.
- W4313532597 hasRelatedWork W4308191010 @default.
- W4313532597 hasRelatedWork W4323021782 @default.
- W4313532597 isParatext "false" @default.
- W4313532597 isRetracted "false" @default.
- W4313532597 workType "book-chapter" @default.