A compressed string matching algorithm for face recognition with partial occlusion
There has been less attention towards the research on face recognition with partial occlusion. Facial accessories such as masks, sunglasses, and caps, etc., cause partial occlusion which results in a significant performance drop of the face recognition system. In this paper, a novel compressed strin...
Gespeichert in:
| Veröffentlicht in: | Multimedia systems Jg. 27; H. 2; S. 191 - 203 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.04.2021
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0942-4962, 1432-1882 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | There has been less attention towards the research on face recognition with partial occlusion. Facial accessories such as masks, sunglasses, and caps, etc., cause partial occlusion which results in a significant performance drop of the face recognition system. In this paper, a novel compressed string matching algorithm based on run-length encoding (CSM-RL) is proposed to solve the partial occlusion problem. In this, the face image is represented by a string sequence that is then compressed using run-length encoding. The proposed CSM-RL algorithm performs string matching between query face and gallery face string sequences by computing the edit distance between string sequences, finally, classifies query face based on the minimum edit distance. The proposed method does not require a classifier and has less time complexity, thus it is more suitable for real-world face recognition applications. The proposed method performs better than the state-of-the-art methods even limited sample images per person are available in the gallery. Extensive experimental results on benchmark face datasets such as AR and Extended Yale-B prove that the proposed algorithm exhibits significant performance improvement both in terms of speed and recognition accuracy for the recognition of partially occluded faces. |
|---|---|
| AbstractList | There has been less attention towards the research on face recognition with partial occlusion. Facial accessories such as masks, sunglasses, and caps, etc., cause partial occlusion which results in a significant performance drop of the face recognition system. In this paper, a novel compressed string matching algorithm based on run-length encoding (CSM-RL) is proposed to solve the partial occlusion problem. In this, the face image is represented by a string sequence that is then compressed using run-length encoding. The proposed CSM-RL algorithm performs string matching between query face and gallery face string sequences by computing the edit distance between string sequences, finally, classifies query face based on the minimum edit distance. The proposed method does not require a classifier and has less time complexity, thus it is more suitable for real-world face recognition applications. The proposed method performs better than the state-of-the-art methods even limited sample images per person are available in the gallery. Extensive experimental results on benchmark face datasets such as AR and Extended Yale-B prove that the proposed algorithm exhibits significant performance improvement both in terms of speed and recognition accuracy for the recognition of partially occluded faces. |
| Author | Bommidi, Krishnaveni Sundaramurthy, Sridhar |
| Author_xml | – sequence: 1 givenname: Krishnaveni surname: Bommidi fullname: Bommidi, Krishnaveni email: krishnaveni@auist.net organization: Information Science and Technology Department, Anna University – sequence: 2 givenname: Sridhar surname: Sundaramurthy fullname: Sundaramurthy, Sridhar organization: Information Science and Technology Department, Anna University |
| BookMark | eNp9kFtLwzAUgINMcJv-AZ8CPkdza9M8juENBoLoc0izZMtom5pkiP_e1gqCD3sICSfnO5dvAWZd6CwA1wTfEozFXcK4YBhhOhwsqEDyDMwJZxSRqqIzMMeSU8RlSS_AIqUDxkSUDM_B6wqa0PbRpmS3MOXoux1sdTb78aGbXYg-71voQoROGwujNWHX-exDBz-HL9jrmL1uYDCmOaYhfAnOnW6Svfq9l-D94f5t_YQ2L4_P69UGGUZkRgzzragscU5QSuraOG45qQXTVSlrUnCipaSy4sLKUhrNHXNO0oIRXmhLt2wJbqa6fQwfR5uyOoRj7IaWihaYVqXgshiy6JRlYkgpWqf66FsdvxTBanSnJndqcKd-3Ck5QNU_yPisx51z1L45jbIJTf3o0sa_qU5Q3y3ShXs |
| CitedBy_id | crossref_primary_10_1007_s11042_023_16086_2 crossref_primary_10_1007_s00530_024_01399_5 crossref_primary_10_1007_s00530_024_01280_5 |
| Cites_doi | 10.1109/TPAMI.2008.79 10.1109/TIP.2017.2713940 10.1109/CVPR.2008.4587598 10.1109/34.927464 10.1109/TIP.2018.2890312 10.1049/iet-bmt.2017.0083 10.1016/S0031-3203(00)00162-X 10.1109/ICCV.2013.80 10.1049/iet-ipr.2017.0757 10.1109/TPAMI.2010.128 10.1109/TIP.2017.2675206 10.1162/neco_a_00990 10.1016/j.patcog.2008.05.024 10.1109/TIFS.2014.2359632 10.1109/TIP.2017.2756450 10.1109/TPAMI.2005.92 10.1109/TNNLS.2018.2836933 10.1007/s11263-014-0722-8 10.1109/CVPRW.2010.5544616 10.1109/CVPR.2007.383052 10.1109/TIP.2016.2515987 10.1016/S0031-3203(01)00023-1 10.1109/AFGR.2008.4813410 10.1109/TIFS.2009.2020772 10.1007/978-3-642-01793-3_31 10.1109/34.598228 10.1109/TIFS.2018.2804919 10.1109/TPAMI.2013.102 10.1109/TIP.2017.2729885 10.1109/TIP.2019.2938307 10.1109/34.41390 10.1109/TIP.2013.2277920 10.1109/TIP.2017.2662213 10.1109/FG.2011.5771439 10.1109/TNN.2002.804287 10.1109/IJCB.2011.6117573 10.1109/CVPR.2009.5206862 10.1109/TNN.2005.849817 10.1109/TIP.2012.2235849 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021. |
| DBID | AAYXX CITATION JQ2 |
| DOI | 10.1007/s00530-020-00727-9 |
| DatabaseName | CrossRef ProQuest Computer Science Collection |
| DatabaseTitle | CrossRef ProQuest Computer Science Collection |
| DatabaseTitleList | ProQuest Computer Science Collection |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1432-1882 |
| EndPage | 203 |
| ExternalDocumentID | 10_1007_s00530_020_00727_9 |
| GroupedDBID | --Z -4Z -59 -5G -BR -EM -ET -Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 203 28- 29M 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5QI 5VS 67Z 6NX 78A 85S 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYOK AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFFNX AFGCZ AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. BA0 BBWZM BDATZ BGNMA BSONS CAG COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EBLON EBS EDO EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ H~9 I-F I09 IHE IJ- IKXTQ ITG ITH ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P9O PF0 PT4 PT5 QF4 QM1 QN7 QO4 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV 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 TN5 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YIN YLTOR Z45 Z7R Z7X Z83 Z88 Z8M Z8R Z8W Z92 ZMTXR ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABJCF ABRTQ ACSTC ADHKG AETEA AEZWR AFDZB AFFHD AFHIU AFKRA AFOHR AGQPQ AHPBZ AHWEU AIXLP ARAPS ATHPR AYFIA BENPR BGLVJ CCPQU CITATION HCIFZ K7- M7S PHGZM PHGZT PQGLB PTHSS JQ2 |
| ID | FETCH-LOGICAL-c319t-304d78e1ff7221bbcf4e41b73a869b1541a9929847e969ca4f3ff9253145ae2d3 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 6 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000604844700006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0942-4962 |
| IngestDate | Thu Sep 25 00:40:02 EDT 2025 Tue Nov 18 22:26:13 EST 2025 Sat Nov 29 03:45:57 EST 2025 Fri Feb 21 02:50:10 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | Biometrics Occlusion Edit distance Face recognition Compressed string Run-length encoding |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-304d78e1ff7221bbcf4e41b73a869b1541a9929847e969ca4f3ff9253145ae2d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2502867495 |
| PQPubID | 2043725 |
| PageCount | 13 |
| ParticipantIDs | proquest_journals_2502867495 crossref_primary_10_1007_s00530_020_00727_9 crossref_citationtrail_10_1007_s00530_020_00727_9 springer_journals_10_1007_s00530_020_00727_9 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-04-01 |
| PublicationDateYYYYMMDD | 2021-04-01 |
| PublicationDate_xml | – month: 04 year: 2021 text: 2021-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Heidelberg |
| PublicationTitle | Multimedia systems |
| PublicationTitleAbbrev | Multimedia Systems |
| PublicationYear | 2021 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
| References | BelhumeurPNHespanhaJPKriegmanDJEigenfaces vs. fishy faces: recognition using class specific linear projectionIEEE Trans. Pattern Anal. Mach. Intell.199719771172010.1109/34.598228 YueqiDLuJFengJZhouJTopology preserving structural matching for automatic partial face recognitionIEEE Trans. Inf. Forensics Secur.20181371823183710.1109/TIFS.2018.2804919 LuJWangGZhouJSimultaneous feature and dictionary learning for image set based face recognitionIEEE Trans. Image Process.20172640424054366654210.1109/TIP.2017.2713940 ChenWGaoYRecognizing partially occluded faces from a single sample per class using string-based matchingProc. Eur. Conf. Comput. Vis.20103496509 VuTHMongaVFast low-rank shared dictionary learning for image classificationIEEE Trans. Image Process.2017261151605175369076910.1109/TIP.2017.2729885 Meng, Y., Lei, Z., Jian, Y., et al.: Robust sparse coding for face recognition. In: Proceedings IEEE International Conference Computer Vision Pattern Recognition, pp. 625–632 (2011) Turk, M. A., Pentland, A. P., Face recognition using eigenfaces. In: Proceedings of the IEEE Conference on CVPR, pp. 586–591 (1991) LeeKHoJKriegmanDAcquiring Linear subspaces for face recognition under variable lightingIEEE Trans. Pattern Anal. Mach. Intell.200527568469810.1109/TPAMI.2005.92 GeorghiadesABelhumeurPKriegmanDFrom few to many: Illumination cone models for face recognition under variable lighting and poseIEEE Trans. Pattern Anal. Mach. Intell.200123664366010.1109/34.927464 TanXChenSZhouZHRecognizing partially occluded, expression variant faces from a single training image per person with SOM and soft k-NN ensembleIEEE Trans. Neural Netw.200516487588610.1109/TNN.2005.849817 FritzKDamianaLSerenaMA robust group sparse representation variational method with applications to face recognitionIEEE Trans. Image Process.201928627852798393754510.1109/TIP.2018.2890312 Mario, F.: Face recognition using approximate string matching. Program Studi Teknik Informatika, Sekolah Teknik Electro dan Informatika ITB, Corpus (2014) ChangCCLinCJLIBSVM: A library for support vector machines ACM TransIntell. Syst. Technol.20112327 Liao, S., Jain, A.K.: Partial face recognition: an alignment-free approach. In: Proceedings of the International Joint Conference on Biometrics (IJCB 11) (2011) LiadisMWangHMolinaRKatsaggelosAKRobust and low-rank representation for fast face identification with occlusionsIEEE Trans. Image Process.201726522032218364042910.1109/TIP.2017.2675206 TanXChenSZhouZHLiuJFace recognition under occlusions and variant expressions with partial similarityIEEE Trans. Inform. Forensics Secur.20094221723010.1109/TIFS.2009.2020772 WeiXLiCTLeiZDynamic image-to-class warping for occluded face recognitionIEEE Trans. Inf. Forensics Secur.20149122035205010.1109/TIFS.2014.2359632 MartinezAMBenaventeRThe AR face databaseTech. Rep.1998245 KirbyMSirovichLApplication of the Karhunen-Loève procedure for the characterization of the human faceIEEE Trans. Pattern Anal. Mach. Intell.199010.1109/34.41390 ChenWGaoYFace recognition using ensemble string matchingIEEE Trans. Image Process.2013221247984808311870510.1109/TIP.2013.2277920 BartlettMSMovellanJRSejnowskiTJFace recognition by independent component analysisIEEE Trans. Neural Netw.2002131450146410.1109/TNN.2002.804287 SuYZheLMengyaoWSparse representation-based face recognition against expression and illuminationIET Image Proc.201812582683210.1049/iet-ipr.2017.0757 YangMZhangLYangJZhangDRegularized robust coding for face recognitionIEEE Trans. Image Process.201322517531766306162010.1109/TIP.2012.2235849 XieJYangJQianJJTaiYZhangHMRobust nuclear norm-based matrix regression with applications to robust face recognitionIEEE Trans. Image Process.201726522862295364043510.1109/TIP.2017.2662213 YangMZhangLFengXSparse representation based Fisher discrimination dictionary learning for image classificationInt. J. Comput. Vis.20141093209232324088110.1007/s11263-014-0722-8 Weng, R., Lu, J., Hu, J., Yang, G., Tan, Y.P.: Robust feature set matching for partial face recognition. In: Proceedings of IEEE International Conference Computer Vision (ICCV), pp. 601–608 (2013) Mehdipour, M., Ghazi, K., Ekenel, H.: A comprehensive analysis of deep learning based representation for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–41(2016) Jia, H., Martinez, A.M.: Support vector machines in face recognition with occlusions. In: Proceedings of the IEEE 10th Scientific World Journal Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR 09), pp. 136–141(2009) Min, R., Hadid, A., Dugelay, J.L.: Improving the recognition of faces occluded by facial accessories. In: Proceedings IEEE International Conference Automatic Face Gesture Recognition (FG), pp. 442–447 (2011) Zhang, L.: Matlab Code for RRC_L1, http://www4.comp.polyu.edu.hk/ (2013). Accessed Dec 2014 Ekenel, H.K., Stiefelhagen, R.: Why is facial occlusion a challenging problem. In: Proceedings IAPR 3rd International Conference Biometrics (ICB), pp. 299–308 (2009) Storer, M., Urschler, M., Bischof, H.: Occlusion detection for ICAO compliant facial photographs. In: Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 122–129 (2010) HuGPengXYangYLearning deep face representations using small dataIEEE Trans. Image Process.201827293303372984910.1109/TIP.2017.2756450 YuHYangJA direct LDA algorithm for high-dimensional data with application to face recognitionPattern Recogn.2001342067207010.1016/S0031-3203(00)00162-X Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: Proceedings of IEEE Conference Computer Vision Pattern Recognition (CVPR), pp. 1–8 (2008) Zhou, E., Cao, Z., Yin, Q.: Naive-Deep face recognition: touching the limit of LFW benchmark or not? (2015). arXiv:1501.04690 WrightJYangAYGaneshARobust face recognition via sparse RepresentationIEEE Trans. Pattern Anal. Mach. Intell.200931221022710.1109/TPAMI.2008.79 WengRLuJTanYPRobust point set matching for partial face recognitionIEEE Trans. Image Process.201625311631176345548410.1109/TIP.2016.2515987 GaoYLeungMKHHuman face profile recognition using attributed stringPattern Recogn.200235235336010.1016/S0031-3203(01)00023-1 Jia, H., Martinez, A.M.: Face recognition with occlusions in the training and testing sets. In: Proceedings IEEE International Conference Automatic Face Gesture Recognition (FG), pp. 1–6 (2008) Zhou, Z., Wagner, A., Mobahi, H., et al.: Face recognition with contiguous occlusion using Markov random fields. IEEE International Conference Computer Vision (ICCV), pp. 1050–1057 (2009) ZhangLLiuJZhangBDeep cascade model-based face recognition: When deep-layered learning meets small dataIEEE Trans. Image Process.20202910161029403074610.1109/TIP.2019.2938307 JiaKChanTHMaYRobust and practical face recognition via structured sparsityProc. Eur. Conf. Comput. Vis. (ECCV)20127575331344 Liadis, M., Wang, H., Molina, R., et al.: https://github.com/miliadis/FIRC2017. Accessed 5 Jan 2018 Lin, D., Tang, X.: Quality-driven face occlusion detection and recovery. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–7 (2007) Zhang, D., Yang, M., Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In: Proceedings of IEEE International Conference Computer Vision (ICCV), pp. 471–478 (2011) BingrongXQingshanLTingwenHA discrete-time projection neural network for sparse signal reconstruction with application to face recognitionIEEE Trans. Neural Netw. Learn. Syst.2019301151162390140110.1109/TNNLS.2018.2836933 GrmKStrucVArtigesAStrengths and weaknesses of deep learning models for face recognition against image degradationsIET Biom.201771818910.1049/iet-bmt.2017.0083 KananHRFaezKGaoYFace recognition using adaptively weighted patch pzm array from a single exemplar image per personPattern Recognit.200841123799381210.1016/j.patcog.2008.05.024 HeRZhengWSTanTSunZHalf-quadratic-based iterative minimization for robust sparse representationIEEE Trans. Pattern Anal. Mach. Intell.201436226127510.1109/TPAMI.2013.102 NaseemAITogneriBRBennamounCMLinear regression for face recognitionIEEE Trans. Pattern Anal. Mach. Intell.201032112106211210.1109/TPAMI.2010.128 NefianAVHayesMHHidden Markov models for face recognitionProc. IEEE Int. Conf. Acoust. Speech Signal Process.1998527212724 RawatWWangADeepZConvolutional neural networks for image classification. A comprehensive reviewNeural Comput.20172923522449386678110.1162/neco_a_00990 X Bingrong (727_CR41) 2019; 30 X Tan (727_CR14) 2009; 4 M Liadis (727_CR38) 2017; 26 AV Nefian (727_CR11) 1998; 5 W Chen (727_CR29) 2013; 22 M Yang (727_CR25) 2014; 109 HR Kanan (727_CR13) 2008; 41 W Rawat (727_CR45) 2017; 29 727_CR21 727_CR20 727_CR22 Y Gao (727_CR31) 2002; 35 R He (727_CR24) 2014; 36 J Wright (727_CR19) 2009; 31 MS Bartlett (727_CR10) 2002; 13 R Weng (727_CR18) 2016; 25 M Yang (727_CR34) 2013; 22 M Kirby (727_CR6) 1990 727_CR39 727_CR36 727_CR35 X Wei (727_CR17) 2014; 9 727_CR32 CC Chang (727_CR53) 2011; 2 L Zhang (727_CR48) 2020; 29 PN Belhumeur (727_CR9) 1997; 19 K Fritz (727_CR28) 2019; 28 K Jia (727_CR23) 2012; 7575 W Chen (727_CR30) 2010; 3 X Tan (727_CR12) 2005; 16 727_CR47 727_CR7 727_CR2 727_CR3 727_CR42 G Hu (727_CR44) 2018; 27 727_CR4 727_CR5 J Lu (727_CR43) 2017; 26 727_CR52 727_CR1 AM Martinez (727_CR49) 1998; 24 Y Su (727_CR27) 2018; 12 J Xie (727_CR37) 2017; 26 A Georghiades (727_CR50) 2001; 23 TH Vu (727_CR26) 2017; 26 D Yueqi (727_CR40) 2018; 13 AI Naseem (727_CR33) 2010; 32 K Lee (727_CR51) 2005; 27 727_CR16 727_CR15 K Grm (727_CR46) 2017; 7 H Yu (727_CR8) 2001; 34 |
| References_xml | – reference: Liadis, M., Wang, H., Molina, R., et al.: https://github.com/miliadis/FIRC2017. Accessed 5 Jan 2018 – reference: TanXChenSZhouZHRecognizing partially occluded, expression variant faces from a single training image per person with SOM and soft k-NN ensembleIEEE Trans. Neural Netw.200516487588610.1109/TNN.2005.849817 – reference: LuJWangGZhouJSimultaneous feature and dictionary learning for image set based face recognitionIEEE Trans. Image Process.20172640424054366654210.1109/TIP.2017.2713940 – reference: NefianAVHayesMHHidden Markov models for face recognitionProc. IEEE Int. Conf. Acoust. Speech Signal Process.1998527212724 – reference: BartlettMSMovellanJRSejnowskiTJFace recognition by independent component analysisIEEE Trans. Neural Netw.2002131450146410.1109/TNN.2002.804287 – reference: ChenWGaoYRecognizing partially occluded faces from a single sample per class using string-based matchingProc. Eur. Conf. Comput. Vis.20103496509 – reference: WrightJYangAYGaneshARobust face recognition via sparse RepresentationIEEE Trans. Pattern Anal. Mach. Intell.200931221022710.1109/TPAMI.2008.79 – reference: BelhumeurPNHespanhaJPKriegmanDJEigenfaces vs. fishy faces: recognition using class specific linear projectionIEEE Trans. Pattern Anal. Mach. Intell.199719771172010.1109/34.598228 – reference: Meng, Y., Lei, Z., Jian, Y., et al.: Robust sparse coding for face recognition. In: Proceedings IEEE International Conference Computer Vision Pattern Recognition, pp. 625–632 (2011) – reference: GrmKStrucVArtigesAStrengths and weaknesses of deep learning models for face recognition against image degradationsIET Biom.201771818910.1049/iet-bmt.2017.0083 – reference: GaoYLeungMKHHuman face profile recognition using attributed stringPattern Recogn.200235235336010.1016/S0031-3203(01)00023-1 – reference: Storer, M., Urschler, M., Bischof, H.: Occlusion detection for ICAO compliant facial photographs. In: Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 122–129 (2010) – reference: Zhou, E., Cao, Z., Yin, Q.: Naive-Deep face recognition: touching the limit of LFW benchmark or not? (2015). arXiv:1501.04690 – reference: MartinezAMBenaventeRThe AR face databaseTech. Rep.1998245 – reference: FritzKDamianaLSerenaMA robust group sparse representation variational method with applications to face recognitionIEEE Trans. Image Process.201928627852798393754510.1109/TIP.2018.2890312 – reference: Zhang, D., Yang, M., Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In: Proceedings of IEEE International Conference Computer Vision (ICCV), pp. 471–478 (2011) – reference: SuYZheLMengyaoWSparse representation-based face recognition against expression and illuminationIET Image Proc.201812582683210.1049/iet-ipr.2017.0757 – reference: YangMZhangLYangJZhangDRegularized robust coding for face recognitionIEEE Trans. Image Process.201322517531766306162010.1109/TIP.2012.2235849 – reference: Min, R., Hadid, A., Dugelay, J.L.: Improving the recognition of faces occluded by facial accessories. In: Proceedings IEEE International Conference Automatic Face Gesture Recognition (FG), pp. 442–447 (2011) – reference: VuTHMongaVFast low-rank shared dictionary learning for image classificationIEEE Trans. Image Process.2017261151605175369076910.1109/TIP.2017.2729885 – reference: Lin, D., Tang, X.: Quality-driven face occlusion detection and recovery. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–7 (2007) – reference: Mario, F.: Face recognition using approximate string matching. Program Studi Teknik Informatika, Sekolah Teknik Electro dan Informatika ITB, Corpus (2014) – reference: LiadisMWangHMolinaRKatsaggelosAKRobust and low-rank representation for fast face identification with occlusionsIEEE Trans. Image Process.201726522032218364042910.1109/TIP.2017.2675206 – reference: ChangCCLinCJLIBSVM: A library for support vector machines ACM TransIntell. Syst. Technol.20112327 – reference: Jia, H., Martinez, A.M.: Support vector machines in face recognition with occlusions. In: Proceedings of the IEEE 10th Scientific World Journal Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR 09), pp. 136–141(2009) – reference: LeeKHoJKriegmanDAcquiring Linear subspaces for face recognition under variable lightingIEEE Trans. Pattern Anal. Mach. Intell.200527568469810.1109/TPAMI.2005.92 – reference: YueqiDLuJFengJZhouJTopology preserving structural matching for automatic partial face recognitionIEEE Trans. Inf. Forensics Secur.20181371823183710.1109/TIFS.2018.2804919 – reference: Jia, H., Martinez, A.M.: Face recognition with occlusions in the training and testing sets. In: Proceedings IEEE International Conference Automatic Face Gesture Recognition (FG), pp. 1–6 (2008) – reference: YangMZhangLFengXSparse representation based Fisher discrimination dictionary learning for image classificationInt. J. Comput. Vis.20141093209232324088110.1007/s11263-014-0722-8 – reference: Mehdipour, M., Ghazi, K., Ekenel, H.: A comprehensive analysis of deep learning based representation for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–41(2016) – reference: NaseemAITogneriBRBennamounCMLinear regression for face recognitionIEEE Trans. Pattern Anal. Mach. Intell.201032112106211210.1109/TPAMI.2010.128 – reference: KananHRFaezKGaoYFace recognition using adaptively weighted patch pzm array from a single exemplar image per personPattern Recognit.200841123799381210.1016/j.patcog.2008.05.024 – reference: Turk, M. A., Pentland, A. P., Face recognition using eigenfaces. In: Proceedings of the IEEE Conference on CVPR, pp. 586–591 (1991) – reference: Zhou, Z., Wagner, A., Mobahi, H., et al.: Face recognition with contiguous occlusion using Markov random fields. IEEE International Conference Computer Vision (ICCV), pp. 1050–1057 (2009) – reference: HeRZhengWSTanTSunZHalf-quadratic-based iterative minimization for robust sparse representationIEEE Trans. Pattern Anal. Mach. Intell.201436226127510.1109/TPAMI.2013.102 – reference: WeiXLiCTLeiZDynamic image-to-class warping for occluded face recognitionIEEE Trans. Inf. Forensics Secur.20149122035205010.1109/TIFS.2014.2359632 – reference: ChenWGaoYFace recognition using ensemble string matchingIEEE Trans. Image Process.2013221247984808311870510.1109/TIP.2013.2277920 – reference: WengRLuJTanYPRobust point set matching for partial face recognitionIEEE Trans. Image Process.201625311631176345548410.1109/TIP.2016.2515987 – reference: Weng, R., Lu, J., Hu, J., Yang, G., Tan, Y.P.: Robust feature set matching for partial face recognition. In: Proceedings of IEEE International Conference Computer Vision (ICCV), pp. 601–608 (2013) – reference: RawatWWangADeepZConvolutional neural networks for image classification. A comprehensive reviewNeural Comput.20172923522449386678110.1162/neco_a_00990 – reference: Zhang, L.: Matlab Code for RRC_L1, http://www4.comp.polyu.edu.hk/ (2013). Accessed Dec 2014 – reference: Ekenel, H.K., Stiefelhagen, R.: Why is facial occlusion a challenging problem. In: Proceedings IAPR 3rd International Conference Biometrics (ICB), pp. 299–308 (2009) – reference: BingrongXQingshanLTingwenHA discrete-time projection neural network for sparse signal reconstruction with application to face recognitionIEEE Trans. Neural Netw. Learn. Syst.2019301151162390140110.1109/TNNLS.2018.2836933 – reference: JiaKChanTHMaYRobust and practical face recognition via structured sparsityProc. Eur. Conf. Comput. Vis. (ECCV)20127575331344 – reference: Liao, S., Jain, A.K.: Partial face recognition: an alignment-free approach. In: Proceedings of the International Joint Conference on Biometrics (IJCB 11) (2011) – reference: YuHYangJA direct LDA algorithm for high-dimensional data with application to face recognitionPattern Recogn.2001342067207010.1016/S0031-3203(00)00162-X – reference: TanXChenSZhouZHLiuJFace recognition under occlusions and variant expressions with partial similarityIEEE Trans. Inform. Forensics Secur.20094221723010.1109/TIFS.2009.2020772 – reference: XieJYangJQianJJTaiYZhangHMRobust nuclear norm-based matrix regression with applications to robust face recognitionIEEE Trans. Image Process.201726522862295364043510.1109/TIP.2017.2662213 – reference: ZhangLLiuJZhangBDeep cascade model-based face recognition: When deep-layered learning meets small dataIEEE Trans. Image Process.20202910161029403074610.1109/TIP.2019.2938307 – reference: HuGPengXYangYLearning deep face representations using small dataIEEE Trans. Image Process.201827293303372984910.1109/TIP.2017.2756450 – reference: GeorghiadesABelhumeurPKriegmanDFrom few to many: Illumination cone models for face recognition under variable lighting and poseIEEE Trans. Pattern Anal. Mach. Intell.200123664366010.1109/34.927464 – reference: Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: Proceedings of IEEE Conference Computer Vision Pattern Recognition (CVPR), pp. 1–8 (2008) – reference: KirbyMSirovichLApplication of the Karhunen-Loève procedure for the characterization of the human faceIEEE Trans. Pattern Anal. Mach. Intell.199010.1109/34.41390 – volume: 31 start-page: 210 issue: 2 year: 2009 ident: 727_CR19 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2008.79 – volume: 26 start-page: 4042 year: 2017 ident: 727_CR43 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2713940 – ident: 727_CR52 doi: 10.1109/CVPR.2008.4587598 – volume: 23 start-page: 643 issue: 6 year: 2001 ident: 727_CR50 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.927464 – volume: 28 start-page: 2785 issue: 6 year: 2019 ident: 727_CR28 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2018.2890312 – volume: 7 start-page: 81 issue: 1 year: 2017 ident: 727_CR46 publication-title: IET Biom. doi: 10.1049/iet-bmt.2017.0083 – volume: 2 start-page: 27 issue: 3 year: 2011 ident: 727_CR53 publication-title: Intell. Syst. Technol. – volume: 34 start-page: 2067 year: 2001 ident: 727_CR8 publication-title: Pattern Recogn. doi: 10.1016/S0031-3203(00)00162-X – volume: 5 start-page: 2721 year: 1998 ident: 727_CR11 publication-title: Proc. IEEE Int. Conf. Acoust. Speech Signal Process. – ident: 727_CR20 – ident: 727_CR47 – ident: 727_CR16 doi: 10.1109/ICCV.2013.80 – volume: 12 start-page: 826 issue: 5 year: 2018 ident: 727_CR27 publication-title: IET Image Proc. doi: 10.1049/iet-ipr.2017.0757 – volume: 32 start-page: 2106 issue: 11 year: 2010 ident: 727_CR33 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2010.128 – volume: 3 start-page: 496 year: 2010 ident: 727_CR30 publication-title: Proc. Eur. Conf. Comput. Vis. – volume: 26 start-page: 2203 issue: 5 year: 2017 ident: 727_CR38 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2675206 – volume: 29 start-page: 2352 year: 2017 ident: 727_CR45 publication-title: Neural Comput. doi: 10.1162/neco_a_00990 – volume: 41 start-page: 3799 issue: 12 year: 2008 ident: 727_CR13 publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2008.05.024 – volume: 9 start-page: 2035 issue: 12 year: 2014 ident: 727_CR17 publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2014.2359632 – volume: 27 start-page: 293 year: 2018 ident: 727_CR44 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2756450 – ident: 727_CR32 – volume: 27 start-page: 684 issue: 5 year: 2005 ident: 727_CR51 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.92 – volume: 30 start-page: 151 issue: 1 year: 2019 ident: 727_CR41 publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2018.2836933 – volume: 109 start-page: 209 issue: 3 year: 2014 ident: 727_CR25 publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-014-0722-8 – ident: 727_CR36 – ident: 727_CR5 doi: 10.1109/CVPRW.2010.5544616 – ident: 727_CR4 doi: 10.1109/CVPR.2007.383052 – volume: 25 start-page: 1163 issue: 3 year: 2016 ident: 727_CR18 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2016.2515987 – ident: 727_CR22 – volume: 24 start-page: 5 year: 1998 ident: 727_CR49 publication-title: Tech. Rep. – ident: 727_CR39 – volume: 35 start-page: 353 issue: 2 year: 2002 ident: 727_CR31 publication-title: Pattern Recogn. doi: 10.1016/S0031-3203(01)00023-1 – ident: 727_CR3 doi: 10.1109/AFGR.2008.4813410 – volume: 4 start-page: 217 issue: 2 year: 2009 ident: 727_CR14 publication-title: IEEE Trans. Inform. Forensics Secur. doi: 10.1109/TIFS.2009.2020772 – ident: 727_CR1 doi: 10.1007/978-3-642-01793-3_31 – volume: 19 start-page: 711 issue: 7 year: 1997 ident: 727_CR9 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.598228 – volume: 13 start-page: 1823 issue: 7 year: 2018 ident: 727_CR40 publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2018.2804919 – volume: 36 start-page: 261 issue: 2 year: 2014 ident: 727_CR24 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2013.102 – volume: 26 start-page: 5160 issue: 11 year: 2017 ident: 727_CR26 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2729885 – volume: 29 start-page: 1016 year: 2020 ident: 727_CR48 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2019.2938307 – ident: 727_CR35 – year: 1990 ident: 727_CR6 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.41390 – volume: 22 start-page: 4798 issue: 12 year: 2013 ident: 727_CR29 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2013.2277920 – volume: 26 start-page: 2286 issue: 5 year: 2017 ident: 727_CR37 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2662213 – ident: 727_CR42 – volume: 7575 start-page: 331 year: 2012 ident: 727_CR23 publication-title: Proc. Eur. Conf. Comput. Vis. (ECCV) – ident: 727_CR2 doi: 10.1109/FG.2011.5771439 – volume: 13 start-page: 1450 year: 2002 ident: 727_CR10 publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2002.804287 – ident: 727_CR21 doi: 10.1109/IJCB.2011.6117573 – ident: 727_CR15 doi: 10.1109/CVPR.2009.5206862 – ident: 727_CR7 – volume: 16 start-page: 875 issue: 4 year: 2005 ident: 727_CR12 publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2005.849817 – volume: 22 start-page: 1753 issue: 5 year: 2013 ident: 727_CR34 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2012.2235849 |
| SSID | ssj0017630 |
| Score | 2.293679 |
| Snippet | There has been less attention towards the research on face recognition with partial occlusion. Facial accessories such as masks, sunglasses, and caps, etc.,... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 191 |
| SubjectTerms | Algorithms Computer Communication Networks Computer Graphics Computer Science Cryptology Data Storage Representation Face recognition Occlusion Operating Systems Regular Paper String matching Sunglasses |
| Title | A compressed string matching algorithm for face recognition with partial occlusion |
| URI | https://link.springer.com/article/10.1007/s00530-020-00727-9 https://www.proquest.com/docview/2502867495 |
| Volume | 27 |
| WOSCitedRecordID | wos000604844700006&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: PRVAVX databaseName: Springer Journals New Starts & Take-Overs Collection customDbUrl: eissn: 1432-1882 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017630 issn: 0942-4962 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/eLvHCXMwnV07T8MwED5BYWChPEWhIA9sYClxHDseK0TFgCrES90ix7ELUkmqPvj92G6SCgRIMMexk_Od7_P5_B3AeRjrLE-4wSKSFFMhJbZOL8bO1etAMWak55m95YNBMhyKu-pS2KzOdq-PJP1K3Vx2c_oSYLfdcXTXHIt12Igd24zboz88N2cH1mJ8ZEVQYgdnpLoq830fn93RCmN-ORb13qbf_t937sB2hS5Rb6kOu7Cmiz1o15UbUGXI-3DfQy6X3BOH58iV7ihGyGJXn1iJ5HhUTl_nL2_IIlpkpNKoyTMqC-RCt2jiVM4OVSo1XriI2wE89a8fr25wVV0BK2t2cxwFNOeJDo3hhIRZpgzVNMx4JBMmMousQiksdrLeSwsmlKQmMkYQa7M0lprk0SG0irLQR4BiKRw1WsgsvqAq1sL-dkwyrnIeiITJDoS1kFNVUY-7ChjjtCFN9kJLrdBSL7RUdOCieWeyJN74tXW3nru0MsJZatEdSRi3W8AOXNZztXr8c2_Hf2t-AlvEZbr4fJ4utObThT6FTfU-f51Nz7xyfgA_R9vA |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwED90Cvri_MTp1Dz4poE1TZvmcYhj4hyiU3wraZrMwezGPvz7TbK2Q1FBn5sm7eUu98vl8juAcy9QSRoxjbkvKKZcCGycXoCtq1cNGYZaOJ7ZDut2o5cXfp9fCpsW2e7FkaRbqcvLblZfGthudyzdNcN8FdaoLbNj9-iPz-XZgbEYF1nhlJjBQ5Jflfm-j8_uaIkxvxyLOm_Tqv7vO7dhK0eXqLlQhx1YUdkuVIvKDSg35D14aCKbS-6Iw1NkS3dkfWSwq0usRGLYH00Gs9c3ZBAt0kIqVOYZjTJkQ7dobFXODDWScji3Ebd9eGpd967aOK-ugKUxuxn2GzRlkfK0ZoR4SSI1VdRLmC-ikCcGWXmCG-xkvJfiIZeCal9rTozN0kAokvoHUMlGmToEFAhuqdG80OALKgPFzW8HJGEyZQ0ehaIGXiHkWObU47YCxjAuSZOd0GIjtNgJLeY1uCjfGS-IN35tXS_mLs6NcBobdEeikJktYA0ui7laPv65t6O_NT-DjXbvrhN3brq3x7BJbNaLy-2pQ2U2masTWJfvs8F0cuoU9QOVLN6k |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB58IV58i-szB28a3KZp0xxFXRRlEV94K2maqLB2l7X6-51k2_pABfHcNE0nM8yXzMw3ADtBZLI8EZbKUHHKpVIUnV5Enas3bR3HVnme2XPR7SZ3d_LiQxW_z3avQ5KjmgbH0lSU-4Pc7jeFb0532tQdfRz1taByHCY5nmRcUtfl1W0TR0Dr8bcskjNcSMyqspnv5_jsmt7x5pcQqfc8nbn_r3keZivUSQ5GarIAY6ZYhLm6owOpDHwJLg-IyzH3hOI5cS09inuCmNYnXBLVu-8PH8uHJ4JIl1ilDWnyj_oFcVe6ZOBUET_V17r34m7iluGmc3x9eEKrrgtUozmWNGzzXCQmsFYwFmSZttzwIBOhSmKZIeIKlERMhV7NyFhqxW1orWRoyzxShuXhCkwU_cKsAomUdJRpQYy4g-vISPztiGVC56Itk1i1IKgFnuqKktx1xuilDZmyF1qKQku90FLZgt3mncGIkOPX0Rv1PqaVcT6niPpYEgs8GrZgr96398c_z7b2t-HbMH1x1EnPT7tn6zDDXDKMT_nZgIly-GI2YUq_lo_Pwy2vs28PsOeI |
| 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=A+compressed+string+matching+algorithm+for+face+recognition+with+partial+occlusion&rft.jtitle=Multimedia+systems&rft.au=Bommidi%2C+Krishnaveni&rft.au=Sundaramurthy%2C+Sridhar&rft.date=2021-04-01&rft.pub=Springer+Berlin+Heidelberg&rft.issn=0942-4962&rft.eissn=1432-1882&rft.volume=27&rft.issue=2&rft.spage=191&rft.epage=203&rft_id=info:doi/10.1007%2Fs00530-020-00727-9&rft.externalDocID=10_1007_s00530_020_00727_9 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0942-4962&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0942-4962&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0942-4962&client=summon |