A New Fisher Discriminative K-SVD Algorithm for Face Recognition View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Hao Zheng

ABSTRACT

In a sparse-representation-based face recognition scheme, dictionary learning has attracted growing attention for its good performance. Discriminative K-SVD (D-KSVD) is one of conventional dictionary learning algorithm, which can effectively solve the face recognition problem. However, D-KSVD doesn’t consider the discrimination of the sparse coding coefficients. To address this issue, a new algorithm named Fisher Discriminative K-SVD (FD-KSVD) is proposed. In the new algorithm, the Fisher discrimination criterion is imposed on the sparse coding coefficients to make them discriminative through small within-class scatter and big between-class scatter. The optimization is employed by the Iterative Projective Method and K-SVD method alternatively. The experimental results of face databases indicated recognition performance of the new algorithm is superior to other state-of-the-art algorithms. More... »

PAGES

20-28

References to SciGraph publications

Book

TITLE

Intelligence Science and Big Data Engineering. Image and Video Data Engineering

ISBN

978-3-319-23987-3
978-3-319-23989-7

Author Affiliations

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-23989-7_3

DOI

http://dx.doi.org/10.1007/978-3-319-23989-7_3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033927791


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Nanjing University", 
          "id": "https://www.grid.ac/institutes/grid.41156.37", 
          "name": [
            "Key Laboratory of Trusted Cloud Computing and Big Data Analysis, Nanjing XiaoZhuang University", 
            "State Key Laboratory for Novel Software Technology, Nanjing University", 
            "Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology", 
            "Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zheng", 
        "givenName": "Hao", 
        "id": "sg:person.07575706553.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07575706553.15"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-642-15561-1_12", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042033297", 
          "https://doi.org/10.1007/978-3-642-15561-1_12"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-15561-1_12", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042033297", 
          "https://doi.org/10.1007/978-3-642-15561-1_12"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2011.2157359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717906"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2012.2197827", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718104"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2005.92", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061742947"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2008.79", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061743675"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsp.2006.881199", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061800223"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsp.2012.2190406", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061803217"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/s1064827596304010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062884436"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2010.5539989", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093573977"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2011.6126277", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094816098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2008.4587652", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094909522"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2011.5995556", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095361975"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2008.4587408", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095439310"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015", 
    "datePublishedReg": "2015-01-01", 
    "description": "In a sparse-representation-based face recognition scheme, dictionary learning has attracted growing attention for its good performance. Discriminative K-SVD (D-KSVD) is one of conventional dictionary learning algorithm, which can effectively solve the face recognition problem. However, D-KSVD doesn\u2019t consider the discrimination of the sparse coding coefficients. To address this issue, a new algorithm named Fisher Discriminative K-SVD (FD-KSVD) is proposed. In the new algorithm, the Fisher discrimination criterion is imposed on the sparse coding coefficients to make them discriminative through small within-class scatter and big between-class scatter. The optimization is employed by the Iterative Projective Method and K-SVD method alternatively. The experimental results of face databases indicated recognition performance of the new algorithm is superior to other state-of-the-art algorithms.", 
    "editor": [
      {
        "familyName": "He", 
        "givenName": "Xiaofei", 
        "type": "Person"
      }, 
      {
        "familyName": "Gao", 
        "givenName": "Xinbo", 
        "type": "Person"
      }, 
      {
        "familyName": "Zhang", 
        "givenName": "Yanning", 
        "type": "Person"
      }, 
      {
        "familyName": "Zhou", 
        "givenName": "Zhi-Hua", 
        "type": "Person"
      }, 
      {
        "familyName": "Liu", 
        "givenName": "Zhi-Yong", 
        "type": "Person"
      }, 
      {
        "familyName": "Fu", 
        "givenName": "Baochuan", 
        "type": "Person"
      }, 
      {
        "familyName": "Hu", 
        "givenName": "Fuyuan", 
        "type": "Person"
      }, 
      {
        "familyName": "Zhang", 
        "givenName": "Zhancheng", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-23989-7_3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7181075", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": {
      "isbn": [
        "978-3-319-23987-3", 
        "978-3-319-23989-7"
      ], 
      "name": "Intelligence Science and Big Data Engineering. Image and Video Data Engineering", 
      "type": "Book"
    }, 
    "name": "A New Fisher Discriminative K-SVD Algorithm for Face Recognition", 
    "pagination": "20-28", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-23989-7_3"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "a19f6f6504fe503f72565c8bd4f0b13ab6a05e1cc3742f1c40aa842f2a9fa4df"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1033927791"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-23989-7_3", 
      "https://app.dimensions.ai/details/publication/pub.1033927791"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T21:59", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8693_00000264.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-23989-7_3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-23989-7_3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-23989-7_3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-23989-7_3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-23989-7_3'


 

This table displays all metadata directly associated to this object as RDF triples.

145 TRIPLES      23 PREDICATES      40 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-23989-7_3 schema:about anzsrc-for:17
2 anzsrc-for:1701
3 schema:author N7300e3bc08ad471ea6807587e40cc550
4 schema:citation sg:pub.10.1007/978-3-642-15561-1_12
5 https://doi.org/10.1109/cvpr.2008.4587408
6 https://doi.org/10.1109/cvpr.2008.4587652
7 https://doi.org/10.1109/cvpr.2010.5539989
8 https://doi.org/10.1109/cvpr.2011.5995556
9 https://doi.org/10.1109/iccv.2011.6126277
10 https://doi.org/10.1109/tnn.2011.2157359
11 https://doi.org/10.1109/tnnls.2012.2197827
12 https://doi.org/10.1109/tpami.2005.92
13 https://doi.org/10.1109/tpami.2008.79
14 https://doi.org/10.1109/tsp.2006.881199
15 https://doi.org/10.1109/tsp.2012.2190406
16 https://doi.org/10.1137/s1064827596304010
17 schema:datePublished 2015
18 schema:datePublishedReg 2015-01-01
19 schema:description In a sparse-representation-based face recognition scheme, dictionary learning has attracted growing attention for its good performance. Discriminative K-SVD (D-KSVD) is one of conventional dictionary learning algorithm, which can effectively solve the face recognition problem. However, D-KSVD doesn’t consider the discrimination of the sparse coding coefficients. To address this issue, a new algorithm named Fisher Discriminative K-SVD (FD-KSVD) is proposed. In the new algorithm, the Fisher discrimination criterion is imposed on the sparse coding coefficients to make them discriminative through small within-class scatter and big between-class scatter. The optimization is employed by the Iterative Projective Method and K-SVD method alternatively. The experimental results of face databases indicated recognition performance of the new algorithm is superior to other state-of-the-art algorithms.
20 schema:editor Nc12c8f90cfbf4e45887ce5f561f41f2b
21 schema:genre chapter
22 schema:inLanguage en
23 schema:isAccessibleForFree false
24 schema:isPartOf N6d65afef2cb5452a9b5ba31c76cccbab
25 schema:name A New Fisher Discriminative K-SVD Algorithm for Face Recognition
26 schema:pagination 20-28
27 schema:productId N0c34d4340b5f4db2b6cb061bb2981c06
28 N3bd74a83523040b2ad217da6cda3c56a
29 Nee4648c273ac4b858f1a44409b7d2fd5
30 schema:publisher N2744c4b401df4935bf7aa91def5f0706
31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033927791
32 https://doi.org/10.1007/978-3-319-23989-7_3
33 schema:sdDatePublished 2019-04-15T21:59
34 schema:sdLicense https://scigraph.springernature.com/explorer/license/
35 schema:sdPublisher Na95e93c238254a8aa15148b3a597b79f
36 schema:url http://link.springer.com/10.1007/978-3-319-23989-7_3
37 sgo:license sg:explorer/license/
38 sgo:sdDataset chapters
39 rdf:type schema:Chapter
40 N0c34d4340b5f4db2b6cb061bb2981c06 schema:name doi
41 schema:value 10.1007/978-3-319-23989-7_3
42 rdf:type schema:PropertyValue
43 N0e36bdc7f3af4ba58e08ef53538244ef schema:familyName Zhou
44 schema:givenName Zhi-Hua
45 rdf:type schema:Person
46 N2744c4b401df4935bf7aa91def5f0706 schema:location Cham
47 schema:name Springer International Publishing
48 rdf:type schema:Organisation
49 N3802eb58ddee403098e5ebaa8d28a9b1 rdf:first N6d39b3609ea94df28f0adfcc9d7f36c5
50 rdf:rest N445eb1cce5cc4343a6d87134d02c5e51
51 N3bd74a83523040b2ad217da6cda3c56a schema:name readcube_id
52 schema:value a19f6f6504fe503f72565c8bd4f0b13ab6a05e1cc3742f1c40aa842f2a9fa4df
53 rdf:type schema:PropertyValue
54 N445eb1cce5cc4343a6d87134d02c5e51 rdf:first N57a5e39ce9c546cb99979136f2df038c
55 rdf:rest rdf:nil
56 N57a5e39ce9c546cb99979136f2df038c schema:familyName Zhang
57 schema:givenName Zhancheng
58 rdf:type schema:Person
59 N6d39b3609ea94df28f0adfcc9d7f36c5 schema:familyName Hu
60 schema:givenName Fuyuan
61 rdf:type schema:Person
62 N6d65afef2cb5452a9b5ba31c76cccbab schema:isbn 978-3-319-23987-3
63 978-3-319-23989-7
64 schema:name Intelligence Science and Big Data Engineering. Image and Video Data Engineering
65 rdf:type schema:Book
66 N6fc23b75db6547c78531c5c3daf43ef6 schema:familyName Gao
67 schema:givenName Xinbo
68 rdf:type schema:Person
69 N7300e3bc08ad471ea6807587e40cc550 rdf:first sg:person.07575706553.15
70 rdf:rest rdf:nil
71 N89115f9661be4fa89d2f7f96fec0fdb4 schema:familyName Zhang
72 schema:givenName Yanning
73 rdf:type schema:Person
74 N895d6bd67a2c421e9ce2fa73c84c590a rdf:first Nfb62d06cd1754661bfe9554ec5a7fe72
75 rdf:rest Nad023eb34c3e43c7848789d0a47798e9
76 Na95e93c238254a8aa15148b3a597b79f schema:name Springer Nature - SN SciGraph project
77 rdf:type schema:Organization
78 Nad023eb34c3e43c7848789d0a47798e9 rdf:first Nb4f3fdd134354fe6b85d26c73105d848
79 rdf:rest N3802eb58ddee403098e5ebaa8d28a9b1
80 Nb4f3fdd134354fe6b85d26c73105d848 schema:familyName Fu
81 schema:givenName Baochuan
82 rdf:type schema:Person
83 Nc12c8f90cfbf4e45887ce5f561f41f2b rdf:first Ne6b97f1037aa47b881a49fcede808698
84 rdf:rest Nf6091d88b24541e7bbfbba96050404e5
85 Ndf975faa37e1428bbe1a2fa566bd61bf rdf:first N89115f9661be4fa89d2f7f96fec0fdb4
86 rdf:rest Ne32be70aa90f4c2f851ba6cb2790e75b
87 Ne32be70aa90f4c2f851ba6cb2790e75b rdf:first N0e36bdc7f3af4ba58e08ef53538244ef
88 rdf:rest N895d6bd67a2c421e9ce2fa73c84c590a
89 Ne6b97f1037aa47b881a49fcede808698 schema:familyName He
90 schema:givenName Xiaofei
91 rdf:type schema:Person
92 Nee4648c273ac4b858f1a44409b7d2fd5 schema:name dimensions_id
93 schema:value pub.1033927791
94 rdf:type schema:PropertyValue
95 Nf6091d88b24541e7bbfbba96050404e5 rdf:first N6fc23b75db6547c78531c5c3daf43ef6
96 rdf:rest Ndf975faa37e1428bbe1a2fa566bd61bf
97 Nfb62d06cd1754661bfe9554ec5a7fe72 schema:familyName Liu
98 schema:givenName Zhi-Yong
99 rdf:type schema:Person
100 anzsrc-for:17 schema:inDefinedTermSet anzsrc-for:
101 schema:name Psychology and Cognitive Sciences
102 rdf:type schema:DefinedTerm
103 anzsrc-for:1701 schema:inDefinedTermSet anzsrc-for:
104 schema:name Psychology
105 rdf:type schema:DefinedTerm
106 sg:grant.7181075 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-319-23989-7_3
107 rdf:type schema:MonetaryGrant
108 sg:person.07575706553.15 schema:affiliation https://www.grid.ac/institutes/grid.41156.37
109 schema:familyName Zheng
110 schema:givenName Hao
111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07575706553.15
112 rdf:type schema:Person
113 sg:pub.10.1007/978-3-642-15561-1_12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042033297
114 https://doi.org/10.1007/978-3-642-15561-1_12
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1109/cvpr.2008.4587408 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095439310
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1109/cvpr.2008.4587652 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094909522
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1109/cvpr.2010.5539989 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093573977
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1109/cvpr.2011.5995556 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095361975
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1109/iccv.2011.6126277 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094816098
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1109/tnn.2011.2157359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717906
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1109/tnnls.2012.2197827 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718104
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1109/tpami.2005.92 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742947
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1109/tpami.2008.79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743675
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1109/tsp.2006.881199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061800223
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1109/tsp.2012.2190406 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061803217
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1137/s1064827596304010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062884436
139 rdf:type schema:CreativeWork
140 https://www.grid.ac/institutes/grid.41156.37 schema:alternateName Nanjing University
141 schema:name Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology
142 Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education
143 Key Laboratory of Trusted Cloud Computing and Big Data Analysis, Nanjing XiaoZhuang University
144 State Key Laboratory for Novel Software Technology, Nanjing University
145 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...