Random Sampling for Subspace Face Recognition
Issue Title: Special Issue: Computer Vision and Pattern Recognition-CVPR 2004 Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of purs...
Uloženo v:
| Vydáno v: | International journal of computer vision Ročník 70; číslo 1; s. 91 - 104 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer Nature B.V
01.10.2006
|
| Témata: | |
| ISSN: | 0920-5691, 1573-1405 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Issue Title: Special Issue: Computer Vision and Pattern Recognition-CVPR 2004 Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.[PUBLICATION ABSTRACT] |
|---|---|
| AbstractList | Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed. Issue Title: Special Issue: Computer Vision and Pattern Recognition-CVPR 2004 Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.[PUBLICATION ABSTRACT] |
| Author | Wang, Xiaogang Tang, Xiaoou |
| Author_xml | – sequence: 1 givenname: Xiaogang surname: Wang fullname: Wang, Xiaogang – sequence: 2 givenname: Xiaoou surname: Tang fullname: Tang, Xiaoou |
| BookMark | eNp9kD1rwzAQhkVJoUnaH9DNUOim9k6yZGssoV8QKCTtLBRZDg625Fr20Pz62qRThi53y_Me7z0LMvPBO0JuER4QIHuMiExyCiBpDiqnxwsyR5FxiimIGZmDYkCFVHhFFjEeAIDljM8J3RhfhCbZmqatK79PytAl22EXW2Nd8jKNjbNh76u-Cv6aXJamju7mby_J18vz5-qNrj9e31dPa2p5Bj0trcgLyZkpjbCQo8Id2NSUkpfAlEF0KWdSZgDcgNw5zHPBC6l4kYqMMcuX5P50t-3C9-Bir5sqWlfXxrswRM0USMhTPoJ3Z-AhDJ0fu2lEHMsoycVIZSfKdiHGzpXaVr2ZHuo7U9UaQU8S9UmiHiXqSaI-jkk8S7Zd1Zju55_ML4OPc_A |
| CitedBy_id | crossref_primary_10_1016_j_patcog_2011_08_024 crossref_primary_10_1007_s00521_017_3092_7 crossref_primary_10_1109_TIM_2019_2905904 crossref_primary_10_1109_TPAMI_2012_70 crossref_primary_10_1016_j_knosys_2018_02_008 crossref_primary_10_1109_TPAMI_2008_222 crossref_primary_10_1016_j_ins_2015_03_063 crossref_primary_10_1016_j_patcog_2023_110218 crossref_primary_10_1016_j_sigpro_2018_10_015 crossref_primary_10_1109_TPAMI_2014_2366766 crossref_primary_10_1007_s11760_014_0659_y crossref_primary_10_1109_TIFS_2012_2226580 crossref_primary_10_1109_TPAMI_2016_2542816 crossref_primary_10_1016_j_neucom_2016_10_047 crossref_primary_10_1016_j_patcog_2012_04_001 crossref_primary_10_1109_TIP_2015_2460464 crossref_primary_10_4018_jmcmc_2012100102 crossref_primary_10_1016_j_ecohyd_2023_01_001 crossref_primary_10_1109_TNN_2007_894042 crossref_primary_10_1016_j_cviu_2015_02_011 crossref_primary_10_1016_j_imavis_2008_12_009 crossref_primary_10_1007_s11760_012_0411_4 crossref_primary_10_1007_s11263_008_0161_5 crossref_primary_10_1016_j_eswa_2010_07_150 crossref_primary_10_1007_s11356_022_22761_y crossref_primary_10_1007_s11063_011_9180_2 crossref_primary_10_1109_TIFS_2009_2025844 crossref_primary_10_1016_j_neucom_2008_04_042 crossref_primary_10_1016_j_inffus_2015_09_005 crossref_primary_10_1016_j_patcog_2017_01_003 crossref_primary_10_1587_transinf_2017PCP0012 crossref_primary_10_1109_TKDE_2010_143 crossref_primary_10_1016_j_patcog_2015_12_011 crossref_primary_10_1016_j_image_2015_06_010 crossref_primary_10_1016_j_neucom_2012_11_015 crossref_primary_10_3390_rs15204952 crossref_primary_10_1049_iet_bmt_2016_0060 crossref_primary_10_1080_10106049_2021_1967464 crossref_primary_10_1109_TNN_2011_2152852 crossref_primary_10_1145_3625235 crossref_primary_10_1109_LSP_2020_3005039 crossref_primary_10_1016_j_neucom_2018_02_100 crossref_primary_10_1016_j_geomorph_2017_12_008 crossref_primary_10_1109_TPAMI_2012_229 crossref_primary_10_1016_j_patrec_2007_10_005 crossref_primary_10_1109_TNNLS_2015_2464681 crossref_primary_10_1016_j_simpat_2016_02_007 crossref_primary_10_1109_TIFS_2011_2156787 crossref_primary_10_1016_j_heliyon_2020_e03751 crossref_primary_10_1016_j_knosys_2016_05_033 crossref_primary_10_1007_s11053_021_09891_9 crossref_primary_10_1109_TIFS_2012_2214212 crossref_primary_10_1109_TPAMI_2011_104 crossref_primary_10_1016_j_patcog_2008_08_025 crossref_primary_10_1016_j_asoc_2011_12_019 crossref_primary_10_1007_s00138_013_0584_z crossref_primary_10_1080_10106049_2021_1892210 crossref_primary_10_1080_1206212X_2016_1160643 crossref_primary_10_3390_su12072622 crossref_primary_10_1016_j_patcog_2016_08_007 crossref_primary_10_1109_JOE_2013_2280035 crossref_primary_10_1016_j_eswa_2013_07_047 crossref_primary_10_1016_j_neucom_2014_11_105 crossref_primary_10_1016_j_eswa_2010_03_025 crossref_primary_10_1155_2009_465193 crossref_primary_10_1109_TPAMI_2008_174 crossref_primary_10_1016_j_patcog_2016_11_016 crossref_primary_10_1109_TIP_2009_2038765 crossref_primary_10_1109_TCSVT_2012_2202079 crossref_primary_10_1109_TIP_2020_3016502 crossref_primary_10_1016_j_patcog_2008_10_014 crossref_primary_10_1016_j_patcog_2009_01_010 crossref_primary_10_1007_s11263_013_0645_9 crossref_primary_10_1109_TCYB_2020_2985997 crossref_primary_10_3390_s141223509 crossref_primary_10_1016_j_patcog_2010_07_014 crossref_primary_10_1109_TPAMI_2013_134 crossref_primary_10_1109_TPAMI_2010_180 crossref_primary_10_1016_j_jvcir_2015_03_001 crossref_primary_10_1016_j_patcog_2017_06_004 crossref_primary_10_1016_j_patcog_2019_107040 crossref_primary_10_3390_rs14194803 crossref_primary_10_1109_TNNLS_2013_2258174 crossref_primary_10_1016_j_neucom_2013_09_007 crossref_primary_10_1016_j_patcog_2017_04_014 crossref_primary_10_1109_TIFS_2014_2360825 crossref_primary_10_3390_f11040421 |
| Cites_doi | 10.1109/34.598231 10.1016/S0167-8655(03)00079-5 10.1109/3477.990871 10.3233/IDA-1999-3304 10.1109/21.155943 10.1109/34.735803 10.1016/S0031-3203(99)00179-X 10.1109/34.709601 10.1109/34.588027 10.1007/3-540-44887-X_101 10.1109/72.788647 10.1007/978-3-642-72201-1_13 10.1007/3-540-48219-9_8 10.1109/CVPR.1991.139758 10.1145/954339.954342 10.1016/S0031-3203(99)00139-9 10.1109/34.531802 10.1109/TPAMI.2004.57 10.1109/34.598235 10.1109/34.598228 |
| ContentType | Journal Article |
| Copyright | Springer Science + Business Media, LLC 2006 |
| Copyright_xml | – notice: Springer Science + Business Media, LLC 2006 |
| DBID | AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8FD 8FE 8FG 8FK 8FL ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PYYUZ Q9U |
| DOI | 10.1007/s11263-006-8098-z |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business (OCUL) ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ABI/INFORM Collection China ProQuest Central Basic |
| DatabaseTitle | CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ABI/INFORM China ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) Business Premium Collection (Alumni) |
| DatabaseTitleList | Computer and Information Systems Abstracts ABI/INFORM Global (Corporate) |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Computer Science |
| EISSN | 1573-1405 |
| EndPage | 104 |
| ExternalDocumentID | 2793896201 10_1007_s11263_006_8098_z |
| Genre | Feature |
| GroupedDBID | -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 2.D 203 28- 29J 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 6TJ 78A 7WY 8FE 8FG 8FL 8TC 8UJ 95- 95. 95~ 96X AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYXX ABAKF ABBBX ABBRH ABBXA ABDBE ABDBF ABDZT ABECU ABFSG ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACIHN ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACSTC ACUHS ACZOJ ADHHG ADHIR ADHKG ADIMF ADKFA ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AETLH AEVLU AEXYK AEZWR AFBBN AFDZB AFEXP AFFHD AFGCZ AFHIU AFKRA AFLOW AFOHR AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHWEU AHYZX AIAKS AIGIU AIIXL AILAN AITGF AIXLP AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG ATHPR AVWKF AXYYD AYFIA AYJHY AZFZN AZQEC B-. B0M BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU CITATION COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EAD EAP EAS EBLON EBS EDO EIOEI EJD EMK EPL ESBYG ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IAO ICD IHE IJ- IKXTQ ISR ITC ITM IWAJR IXC IZIGR IZQ I~X I~Y I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PHGZM PHGZT PQBIZ PQBZA PQGLB PQQKQ PROAC PT4 PT5 QF4 QM1 QN7 QO4 QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TAE TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 ZMTXR ~8M ~EX 3V. 7SC 7XB 8AL 8FD 8FK AESKC JQ2 L.- L7M L~C L~D M0N PKEHL PQEST PQUKI Q9U AAYZH PUEGO |
| ID | FETCH-LOGICAL-c370t-fc58d632afa5c08191b0c4af63f029a11e432667003a06be18853d693d45722c3 |
| IEDL.DBID | M0C |
| ISICitedReferencesCount | 149 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000239828100007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0920-5691 |
| IngestDate | Fri Sep 05 10:00:58 EDT 2025 Tue Nov 04 19:45:17 EST 2025 Sat Nov 29 06:42:23 EST 2025 Tue Nov 18 20:47:43 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | http://www.springer.com/tdm |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c370t-fc58d632afa5c08191b0c4af63f029a11e432667003a06be18853d693d45722c3 |
| Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 |
| PQID | 1113709635 |
| PQPubID | 1456341 |
| PageCount | 14 |
| ParticipantIDs | proquest_miscellaneous_29060843 proquest_journals_1113709635 crossref_citationtrail_10_1007_s11263_006_8098_z crossref_primary_10_1007_s11263_006_8098_z |
| PublicationCentury | 2000 |
| PublicationDate | 2006-10-01 |
| PublicationDateYYYYMMDD | 2006-10-01 |
| PublicationDate_xml | – month: 10 year: 2006 text: 2006-10-01 day: 01 |
| PublicationDecade | 2000 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | International journal of computer vision |
| PublicationYear | 2006 |
| Publisher | Springer Nature B.V |
| Publisher_xml | – name: Springer Nature B.V |
| References | P.N. Belhumeur (8098_CR1) 1997; 19 8098_CR29 8098_CR24 8098_CR23 L. Breiman (8098_CR2) 1996; 24 8098_CR22 S.B. Yacoub (8098_CR28) 1999; 10 8098_CR21 8098_CR5 8098_CR4 A. Ross (8098_CR19) 2003; 24 L. Xu (8098_CR27) 1992; 22 T. Kam Ho (8098_CR7) 1998; 20 L. Chen (8098_CR3) 2000; 33 8098_CR17 8098_CR16 8098_CR18 D. Swets (8098_CR20) 1996; 16 8098_CR13 8098_CR15 W.P. Kegelmeyer (8098_CR8) 1997; 19 8098_CR14 T. Kam Ho (8098_CR6) 1999; 3 8098_CR11 8098_CR10 X. Wang (8098_CR25) 2004; 26 8098_CR9 A. Lanitis (8098_CR12) 1997; 19 L. Wiskott (8098_CR26) 1997; 19 |
| References_xml | – ident: 8098_CR11 – volume: 19 start-page: 743 issue: 7 year: 1997 ident: 8098_CR12 publication-title: IEEE Trans. on PAMI doi: 10.1109/34.598231 – volume: 24 start-page: 2115 year: 2003 ident: 8098_CR19 publication-title: Pattern Recognition Letters doi: 10.1016/S0167-8655(03)00079-5 – ident: 8098_CR24 – ident: 8098_CR10 doi: 10.1109/3477.990871 – volume: 3 start-page: 191 year: 1999 ident: 8098_CR6 publication-title: Intelligent Data Analysis doi: 10.3233/IDA-1999-3304 – ident: 8098_CR22 – volume: 22 start-page: 418 issue: 3 year: 1992 ident: 8098_CR27 publication-title: IEEE Trans. on System, Man, and Cybernetics doi: 10.1109/21.155943 – ident: 8098_CR9 – ident: 8098_CR5 doi: 10.1109/34.735803 – ident: 8098_CR15 doi: 10.1016/S0031-3203(99)00179-X – volume: 20 start-page: 832 issue: 8 year: 1998 ident: 8098_CR7 publication-title: IEEE Trans. on PAMI doi: 10.1109/34.709601 – volume: 19 start-page: 405 issue: 4 year: 1997 ident: 8098_CR8 publication-title: IEEE Trans. on PAMI doi: 10.1109/34.588027 – ident: 8098_CR13 doi: 10.1007/3-540-44887-X_101 – volume: 10 start-page: 1065 issue: 5 year: 1999 ident: 8098_CR28 publication-title: IEEE Transactions on Neural Networks doi: 10.1109/72.788647 – ident: 8098_CR4 – ident: 8098_CR14 – ident: 8098_CR16 – ident: 8098_CR17 doi: 10.1007/978-3-642-72201-1_13 – ident: 8098_CR18 doi: 10.1007/3-540-48219-9_8 – ident: 8098_CR21 doi: 10.1109/CVPR.1991.139758 – ident: 8098_CR29 doi: 10.1145/954339.954342 – ident: 8098_CR23 – volume: 33 start-page: 1713 issue: 10 year: 2000 ident: 8098_CR3 publication-title: Pattern Recognition doi: 10.1016/S0031-3203(99)00139-9 – volume: 16 start-page: 831 issue: 8 year: 1996 ident: 8098_CR20 publication-title: IEEE Trans. on PAMI doi: 10.1109/34.531802 – volume: 24 start-page: 123 issue: 2 year: 1996 ident: 8098_CR2 publication-title: Machine Learning – volume: 26 start-page: 1222 issue: 9 year: 2004 ident: 8098_CR25 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2004.57 – volume: 19 start-page: 775 issue: 7 year: 1997 ident: 8098_CR26 publication-title: IEEE Trans. on Pattern Analysis Machine Intelligence doi: 10.1109/34.598235 – volume: 19 start-page: 711 issue: 7 year: 1997 ident: 8098_CR1 publication-title: IEEE Trans. on PAMI doi: 10.1109/34.598228 |
| SSID | ssj0002823 |
| Score | 2.312512 |
| Snippet | Issue Title: Special Issue: Computer Vision and Pattern Recognition-CVPR 2004 Subspace face recognition often suffers from two problems: (1) the training... Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the... |
| SourceID | proquest crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 91 |
| SubjectTerms | Polls & surveys Studies |
| Title | Random Sampling for Subspace Face Recognition |
| URI | https://www.proquest.com/docview/1113709635 https://www.proquest.com/docview/29060843 |
| Volume | 70 |
| WOSCitedRecordID | wos000239828100007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: ABI/INFORM Collection customDbUrl: eissn: 1573-1405 dateEnd: 20171231 omitProxy: false ssIdentifier: ssj0002823 issn: 0920-5691 databaseCode: 7WY dateStart: 19970101 isFulltext: true titleUrlDefault: https://www.proquest.com/abicomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ABI/INFORM Global customDbUrl: eissn: 1573-1405 dateEnd: 20171231 omitProxy: false ssIdentifier: ssj0002823 issn: 0920-5691 databaseCode: M0C dateStart: 19970101 isFulltext: true titleUrlDefault: https://search.proquest.com/abiglobal providerName: ProQuest – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1573-1405 dateEnd: 20171231 omitProxy: false ssIdentifier: ssj0002823 issn: 0920-5691 databaseCode: P5Z dateStart: 19970101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1573-1405 dateEnd: 20171231 omitProxy: false ssIdentifier: ssj0002823 issn: 0920-5691 databaseCode: K7- dateStart: 19970101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1573-1405 dateEnd: 20171231 omitProxy: false ssIdentifier: ssj0002823 issn: 0920-5691 databaseCode: BENPR dateStart: 19970101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-1405 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002823 issn: 0920-5691 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB7xOnApr1Zs2S45cEKy6th52CfUIlZILavV8ihwiRzHQUhtsiVLD_vrO5N1FnHhwsVSnIesjGfmm4dnAI4sj0qjeM5kEaGBopAVcytS5iIEG2Gci8S1B4V_pqORur3VY-9wa3xaZScTW0Fd1JZ85F-pJXqKeFvGJ9O_jLpGUXTVt9BYhXVCNpTSd8FPl5IYzYlFK3k0keJEh11Usz06FwqKYHIqx6sVm7_WS6_FcqtrhlvvXeU2fPAoM_i22BY7sOKqXdjyiDPw_NzgVNfUoZvbAzYxVVH_CS4N5ZpXDwGi2oDECxrXLhjSMOmSjurqI1wPz65Oz5nvqcAsLmzGShurIpHClCa2BAfCnNvIlIksudAmDF2EgI7O7kjDk9yFCvV5kWgkZZwKYeUnWKvqyu1DkKPqT4STWkmLoKzUQplQmijSKndS6h7w7o9m1hccp74Xv7OXUslEhIxS64gI2bwHx8tXpotqG2893O9okHnGa7IXAvTgcHkbWYbiIKZy9XOTUYV7riL5-e0PHMBm629pM_f6sDZ7enZfYMP-mz02TwNYTX_dDWD9-9loPMGrHykbtLsPx3F8j-Pk8uY_RTjcew |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB5RitReSstDXaAlB7ggWSS2k9gHhBB0BdrtCvGQuAXHcapKbbKQpVX5UfxGZrLxIi7cOPSSg5NYsmc8843nBbBlQ1kaFeZMFBINFIVHMbc8ZU4i2IjinCeuTRQepqORurrSp3Pw4HNhKKzSy8RWUBe1pTvyXWqJniLeFvH--IZR1yjyrvoWGlO2GLh_f9Fka_ZOjpC-25z3v10cHrOuqwCzOMGElTZWRSK4KU1sSSFGeWilKRNRhlybKHISIQ1lrwgTJrmLFGq0ItG4mDjl3Aqc9w28lUKldK4GKZtJfjRfpq3r0SSLEx15L2qbqhdx8piGVP5XK3b_XA8-VwOtbusv_m-78hE-dCg6OJiy_SeYc9USLHaIOujkVYNDvmmFH1sGdmaqov4dnBuKpa9-BIjaAxKfY2Nd0KfHmQ-qqqsVuHyVdazCfFVX7jMEOUKbhDuhlbAIOkvNlYmEkVKr3AmhexB6Cma2K6hOfT1-ZU-loInoGYUOEtGz-x7szH4ZT6uJvPTxhqd51gmWJnsieA82Z69RJJCfx1SuvmsyquAfKinWXp5gE94dX3wfZsOT0WAd3rd3S22U4gbMT27v3BdYsH8mP5vbry2XB3D92gz0CGeKMP4 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT-MwEB7xEuJCeYqyPHKAC1JEYudhH9AKARUIVFU8JMQlOI6DVlqS0pRd0Z-2v25n0riICzcOXHJwEkv2jGe-8bwA9rQX5Ep4qcuzAA0UgUcx1Sx2TYBgww9TFpk6Ufgq7nbF_b3sTcE_mwtDYZVWJtaCOis13ZEfUkv0GPE2Dw_zJiyid9r52X9xqYMUeVptO40xi1yat79ovlVHF6dI633GOme3J-du02HA1TjZ0M11KLKIM5WrUJNy9FNPByqPeO4xqXzfBAhvKJOFKy9KjS9Qu2WRxIWFMWOa47zTMBujjUnhhL3wYaIF0JQZt7FH8yyMpG89qnXans_Ie-pRKWAp3NFHnfhRJdR6rtP6zju0BIsNunaOx8dhGaZMsQKtBmk7jRyrcMg2s7Bjq-BeqyIrn50bRTH2xZODaN4hsdpX2jgdelzbYKuyWIO7L1nHOswUZWE2wEkR8kTMcCm4RjCaSyaUz1UQSJEazmUbPEvNRDeF1qnfx-_kvUQ0MUBCIYXEAMmoDQeTX_rjKiOffbxl6Z80AqdK3onfht3JaxQV5P9RhSlfq4Qq-3si4JufT7AL88g3ydVF9_IHLNRXTnXw4hbMDAevZhvm9J_hr2qwUzO8A49fzT__AYQQOiI |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Random+Sampling+for+Subspace+Face+Recognition&rft.jtitle=International+journal+of+computer+vision&rft.au=Wang%2C+Xiaogang&rft.au=Tang%2C+Xiaoou&rft.date=2006-10-01&rft.pub=Springer+Nature+B.V&rft.issn=0920-5691&rft.eissn=1573-1405&rft.volume=70&rft.issue=1&rft.spage=91&rft_id=info:doi/10.1007%2Fs11263-006-8098-z&rft.externalDBID=HAS_PDF_LINK&rft.externalDocID=2793896201 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0920-5691&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0920-5691&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0920-5691&client=summon |