{"id":{"repo_id":"vcu","oai_identifier":"oai:scholarscompass.vcu.edu:etd-1682"},"canonical_url":"https://search.dev.ndltd.org/etd/vcu/oai:scholarscompass.vcu.edu:etd-1682","repository":{"repo_id":"vcu","name":"Virginia Commonwealth University","base_url":"https://scholarscompass.vcu.edu/do/oai/"},"display":{"title":"Measuring Community Consensus in Facial Characterization Using Spatial Databases and Fuzzy Logic","abstract":"Spatial databases store geometric objects and capture spatial relationships that can be used to represent key features of the human face. One can search spatial databases for these objects, and seek the relationships between them, using fuzzy logic to provide a natural way to describe the human face for the purposes of facial characterization. This study focuses on community perception of short, average, or long nose length. Three algorithms were used to update community opinion of nose length. All three methods showed similar trends in nose length classification which could indicate that the effort to extract spatial data from images to classify nose length is not as crucial as previously thought since community consensus will ultimately give similar results. However, additional testing with larger groups is needed to further validate any conclusion that spatial data can be eliminated.","abstract_html":"Spatial databases store geometric objects and capture spatial relationships that can be used to represent key features of the human face. One can search spatial databases for these objects, and seek the relationships between them, using fuzzy logic to provide a natural way to describe the human face for the purposes of facial characterization. This study focuses on community perception of short, average, or long nose length. Three algorithms were used to update community opinion of nose length. All three methods showed similar trends in nose length classification which could indicate that the effort to extract spatial data from images to classify nose length is not as crucial as previously thought since community consensus will ultimately give similar results. However, additional testing with larger groups is needed to further validate any conclusion that spatial data can be eliminated.","abstract_has_math":false,"creators":["Mastros, James Lee"],"institution":null,"degree_name":"Master of Science","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dr. Lorraine M. Parker"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-01-01T08:00:00Z","date_published":"2005-01-01T08:00:00Z","updated_at":"2026-07-24T05:54:11Z","subjects":["steplock method","nose length","human face","law enforcement","spatial database","criminal justice","fuzzy logic","Computer Sciences","Physical Sciences and Mathematics"],"languages":[],"rights":["© The Author"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarscompass.vcu.edu/etd/683"],"render_values":[{"text":"https://scholarscompass.vcu.edu/etd/683","href":"https://scholarscompass.vcu.edu/etd/683","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25772/8KTD-6R06","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Lorraine M. 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One can search spatial databases for these objects, and seek the relationships between them, using fuzzy logic to provide a natural way to describe the human face for the purposes of facial characterization. This study focuses on community perception of short, average, or long nose length. Three algorithms were used to update community opinion of nose length. All three methods showed similar trends in nose length classification which could indicate that the effort to extract spatial data from images to classify nose length is not as crucial as previously thought since community consensus will ultimately give similar results. However, additional testing with larger groups is needed to further validate any conclusion that spatial data can be eliminated."]},{"key":"dc:title","label":"Title","values":["Measuring Community Consensus in Facial Characterization Using Spatial Databases and Fuzzy Logic"]}]}],"canonical_facts":{"dc:contributor":["Dr. Lorraine M. Parker"],"dc:creator":["Mastros, James Lee"],"dc:date.available":["2014-07-09T07:00:00Z"],"dc:description.abstract":["Spatial databases store geometric objects and capture spatial relationships that can be used to represent key features of the human face. One can search spatial databases for these objects, and seek the relationships between them, using fuzzy logic to provide a natural way to describe the human face for the purposes of facial characterization. This study focuses on community perception of short, average, or long nose length. Three algorithms were used to update community opinion of nose length. All three methods showed similar trends in nose length classification which could indicate that the effort to extract spatial data from images to classify nose length is not as crucial as previously thought since community consensus will ultimately give similar results. However, additional testing with larger groups is needed to further validate any conclusion that spatial data can be eliminated."],"dc:identifier":["https://doi.org/10.25772/8KTD-6R06","https://scholarscompass.vcu.edu/etd/683"],"dc:rights":["© The Author"],"dc:subject":["steplock method","nose length","human face","law enforcement","spatial database","criminal justice","fuzzy logic","Computer Sciences","Physical Sciences and Mathematics"],"dc:title":["Measuring Community Consensus in Facial Characterization Using Spatial Databases and Fuzzy Logic"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T05:54:11Z"}