A Multifaceted Independent Performance Analysis of Facial Subspace Recognition Algorithms
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{"title"=>"A Multifaceted Independent Performance Analysis of Facial Subspace Recognition Algorithms", "type"=>"journal", "authors"=>[{"first_name"=>"Usama Ijaz", "last_name"=>"Bajwa", "scopus_author_id"=>"24490946700"}, {"first_name"=>"Imtiaz Ahmad", "last_name"=>"Taj", "scopus_author_id"=>"36978464900"}, {"first_name"=>"Muhammad Waqas", "last_name"=>"Anwar", "scopus_author_id"=>"35112698000"}, {"first_name"=>"Xuan", "last_name"=>"Wang", "scopus_author_id"=>"56354853800"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84874532890", "doi"=>"10.1371/journal.pone.0056510", "issn"=>"19326203", "pui"=>"368445380", "sgr"=>"84874532890", "pmid"=>"23451054"}, "id"=>"2ccc9056-384e-332c-9dcf-bbb6c78e093b", "abstract"=>"Face recognition has emerged as the fastest growing biometric technology and has expanded a lot in the last few years. Many new algorithms and commercial systems have been proposed and developed. Most of them use Principal Component Analysis (PCA) as a base for their techniques. Different and even conflicting results have been reported by researchers comparing these algorithms. The purpose of this study is to have an independent comparative analysis considering both performance and computational complexity of six appearance based face recognition algorithms namely PCA, 2DPCA, A2DPCA, (2D)(2)PCA, LPP and 2DLPP under equal working conditions. This study was motivated due to the lack of unbiased comprehensive comparative analysis of some recent subspace methods with diverse distance metric combinations. For comparison with other studies, FERET, ORL and YALE databases have been used with evaluation criteria as of FERET evaluations which closely simulate real life scenarios. A comparison of results with previous studies is performed and anomalies are reported. An important contribution of this study is that it presents the suitable performance conditions for each of the algorithms under consideration.", "link"=>"http://www.mendeley.com/research/multifaceted-independent-performance-analysis-facial-subspace-recognition-algorithms", "reader_count"=>7, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Mathematics"=>1, "Neuroscience"=>1, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Neuroscience"=>{"Neuroscience"=>1}, "Computer Science"=>{"Computer Science"=>3}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Taiwan"=>1}, "group_count"=>0}

Scopus | Further Information

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  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"4", "full-text"=>"2", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"5", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Anatomy", "average_usage"=>[248, 440, 562, 666, 765, 856, 944, 1028, 1112, 1198, 1282, 1362, 1430]}, {"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[261, 444, 554, 655, 748, 834, 923, 1004, 1089, 1170, 1244, 1315, 1380]}, {"subject_area"=>"/Medicine and health sciences/Anatomy", "average_usage"=>[248, 441, 564, 668, 769, 859, 948, 1034, 1117, 1202, 1290, 1368, 1437]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[254, 431, 547, 651, 748, 842, 932, 1017, 1098, 1178, 1259, 1336, 1404]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}, {"subject_area"=>"/Social sciences", "average_usage"=>[289, 475, 593, 703, 805, 902, 990, 1078, 1158, 1250, 1336, 1417, 1482]}, {"subject_area"=>"/Social sciences/Psychology", "average_usage"=>[294, 460, 580, 683, 777, 868, 957, 1044, 1124, 1202, 1276, 1356, 1422]}]}
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