Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2012:
Metadata Last Update2020: administrator
Citation KeyFabianPireRoch:2012:SePeTh
TitleSearching for People through Textual and Visual Attributes
FormatDVD, On-line.
Access Date2021, Jan. 28
Number of Files1
Size2208 KiB
Context area
Author1 Fabian, Junior
2 Pires, Ramon
3 Rocha, Anderson
Affiliation1 University of Campinas (Unicamp)
2 University of Campinas (Unicamp)
3 University of Campinas (Unicamp)
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:35 :: -> administrator :: 2012
2020-02-19 02:18:29 :: administrator -> :: 2012
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsFace Search, Rank Fusion, Visual Dictionaries.
AbstractSearching for people through their personal traits has been largely required for several areas and, consequently, has become the center of attention in the scientific community. Locating a suspect or finding missing people in a public space are some of the practical applications which take advantage of research conducted in this topic. In this paper, we propose the use of describable visual attributes (e.g, male, wear glasses, has beard), as labels that can be assigned to an image to describe its appearance. The approach is based on visual dictionaries to generate an intermediate representation for the face images. We train binary classifiers for the attributes which give to each image a score used to obtain its ranking. However, there are some attributes that have no immediate antagonistic (e.g., asian people). Then, we evaluate unary classifiers for such attributes. The method is easily extensible to new attributes. For queries consisting of more than one attribute, we use two approaches of the state-of-the-art to combine the rankings: product of probabilities and rank aggregation. Experimental results show that incorporating visual dictionaries improves the accuracy for some attributes. Furthermore, for many attributes, rank aggregation achieves better results than traditional methods of rank fusion. The proposed solution might be of interest in a forensic scenario for searching suspects in a database by means of textual descriptions provided by a victim.
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