Matches in SemOpenAlex for { <https://semopenalex.org/work/W4320731808> ?p ?o ?g. }
- W4320731808 endingPage "118" @default.
- W4320731808 startingPage "98" @default.
- W4320731808 abstract "The existing single dictionary learning algorithms are applied to face recognition and achieve satisfactory results. However, their performance is poor when dealing with noisy images and images involving complex variations such as large pose variations and occlusions. In this paper, a novel noise-related face image recognition method based on double dictionary transform learning (DDTL) is proposed. On the one hand, DDTL introduces the L2,p-norm to remove the redundant information in the dictionary and the noise involved in the training images, which makes the learned dictionary more discriminative. On the other hand, DDTL introduces the analysis dictionary and performs double dictionary transform learning with the synthetic dictionary. This can better reveal the relationship between the samples and the representation coefficients, and improve the accuracy of the learned dictionary and representation coefficients. Besides, DDTL introduces a linear regression term in the model learning process, which can distinguish and expand the differences between classes. Experimental results on six databases show that DDTL is superior to existing methods." @default.
- W4320731808 created "2023-02-15" @default.
- W4320731808 creator A5030449528 @default.
- W4320731808 creator A5030860096 @default.
- W4320731808 creator A5038805449 @default.
- W4320731808 creator A5063860802 @default.
- W4320731808 date "2023-06-01" @default.
- W4320731808 modified "2023-09-23" @default.
- W4320731808 title "Noise-related face image recognition based on double dictionary transform learning" @default.
- W4320731808 cites W2043080228 @default.
- W4320731808 cites W2057755039 @default.
- W4320731808 cites W2129812935 @default.
- W4320731808 cites W2545426943 @default.
- W4320731808 cites W2898571185 @default.
- W4320731808 cites W2923741314 @default.
- W4320731808 cites W2941635601 @default.
- W4320731808 cites W2963094797 @default.
- W4320731808 cites W2963466847 @default.
- W4320731808 cites W2964454173 @default.
- W4320731808 cites W2975233744 @default.
- W4320731808 cites W2977381419 @default.
- W4320731808 cites W2981264804 @default.
- W4320731808 cites W3000248115 @default.
- W4320731808 cites W3000566195 @default.
- W4320731808 cites W3002477320 @default.
- W4320731808 cites W3003254803 @default.
- W4320731808 cites W3008818343 @default.
- W4320731808 cites W3010775719 @default.
- W4320731808 cites W3022398175 @default.
- W4320731808 cites W3024132664 @default.
- W4320731808 cites W3036149542 @default.
- W4320731808 cites W3082940886 @default.
- W4320731808 cites W3085067733 @default.
- W4320731808 cites W3100168423 @default.
- W4320731808 cites W3119687891 @default.
- W4320731808 cites W3124136285 @default.
- W4320731808 cites W3124191860 @default.
- W4320731808 cites W3129710776 @default.
- W4320731808 cites W3169263889 @default.
- W4320731808 cites W3176307736 @default.
- W4320731808 cites W3179566192 @default.
- W4320731808 cites W3182117591 @default.
- W4320731808 cites W3194949249 @default.
- W4320731808 cites W3203553276 @default.
- W4320731808 cites W3204652201 @default.
- W4320731808 cites W3211747432 @default.
- W4320731808 cites W4214755185 @default.
- W4320731808 cites W4225979812 @default.
- W4320731808 doi "https://doi.org/10.1016/j.ins.2023.02.041" @default.
- W4320731808 hasPublicationYear "2023" @default.
- W4320731808 type Work @default.
- W4320731808 citedByCount "1" @default.
- W4320731808 countsByYear W43207318082023 @default.
- W4320731808 crossrefType "journal-article" @default.
- W4320731808 hasAuthorship W4320731808A5030449528 @default.
- W4320731808 hasAuthorship W4320731808A5030860096 @default.
- W4320731808 hasAuthorship W4320731808A5038805449 @default.
- W4320731808 hasAuthorship W4320731808A5063860802 @default.
- W4320731808 hasConcept C115961682 @default.
- W4320731808 hasConcept C124066611 @default.
- W4320731808 hasConcept C144024400 @default.
- W4320731808 hasConcept C153180895 @default.
- W4320731808 hasConcept C154771677 @default.
- W4320731808 hasConcept C154945302 @default.
- W4320731808 hasConcept C2779304628 @default.
- W4320731808 hasConcept C28490314 @default.
- W4320731808 hasConcept C2988886741 @default.
- W4320731808 hasConcept C31510193 @default.
- W4320731808 hasConcept C36289849 @default.
- W4320731808 hasConcept C41008148 @default.
- W4320731808 hasConcept C97931131 @default.
- W4320731808 hasConcept C99498987 @default.
- W4320731808 hasConceptScore W4320731808C115961682 @default.
- W4320731808 hasConceptScore W4320731808C124066611 @default.
- W4320731808 hasConceptScore W4320731808C144024400 @default.
- W4320731808 hasConceptScore W4320731808C153180895 @default.
- W4320731808 hasConceptScore W4320731808C154771677 @default.
- W4320731808 hasConceptScore W4320731808C154945302 @default.
- W4320731808 hasConceptScore W4320731808C2779304628 @default.
- W4320731808 hasConceptScore W4320731808C28490314 @default.
- W4320731808 hasConceptScore W4320731808C2988886741 @default.
- W4320731808 hasConceptScore W4320731808C31510193 @default.
- W4320731808 hasConceptScore W4320731808C36289849 @default.
- W4320731808 hasConceptScore W4320731808C41008148 @default.
- W4320731808 hasConceptScore W4320731808C97931131 @default.
- W4320731808 hasConceptScore W4320731808C99498987 @default.
- W4320731808 hasLocation W43207318081 @default.
- W4320731808 hasOpenAccess W4320731808 @default.
- W4320731808 hasPrimaryLocation W43207318081 @default.
- W4320731808 hasRelatedWork W1970511339 @default.
- W4320731808 hasRelatedWork W2034369645 @default.
- W4320731808 hasRelatedWork W2061273563 @default.
- W4320731808 hasRelatedWork W2157785665 @default.
- W4320731808 hasRelatedWork W2353840448 @default.
- W4320731808 hasRelatedWork W2522847406 @default.
- W4320731808 hasRelatedWork W2523565831 @default.
- W4320731808 hasRelatedWork W2781836892 @default.
- W4320731808 hasRelatedWork W2893220067 @default.