Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/107508
Title: A Gender Detection Approach
Authors: Del Pozo Bolaños,Marcos 
Travieso, Carlos M. 
Ticay Rivas, Jaime Roberto 
Alonso Hernández, Jesús B. 
UNESCO Clasification: 3307 Tecnología electrónica
Issue Date: 2011
Publisher: IntechOpen
Project: Biomet: Indentificador Multibiometrico Con Metabimetrias 
Abstract: Which are the brain processes that underlie facial identification? What information, among the available in the environment, is used to elaborate a response on a subject's identity? Certainly, our brain uses all the information in greater or less extent. Just focusing on that present on the human face, the system can obtain knowledge about gender, age and ethnicity. This demographic data may not be enough for subject identification, but it definitely gives us some valuable clues. The same can be applied for computer systems. For example, having gender information into account, the system can reduce the pool of the possible identities considerably, making the problem easier and enforcing the final response. Moreover, raw gender information can also be used in fields such as micromarketing and personalized services. A practical example of this can be found on the work presented by Peng and Ding (Peng & Ding 2008). These authors proposed a tree structure system to increase the successful rate of a gender classification. In particular, the system first classify between Asian and Non-Asian ethnicities. Then, two specialized gender classification systems are trained, one for each ethnicity. This resulted in an increase of around 4% over an ordinary system (gender classifier without ethnicity specialization). Therefore, demographic classification systems are as much important and valuables as face identification systems themselves. This is why they have received increasing attention in the last years. In particular, this chapter focuses its attention in facial-base gender-detection systems. A summary of the problem’s characteristics is first given in section 2, along with an overview of the state of art. Section 3 introduces the structure of the system used for experiments of section 4, where we check the effect of preprocessing variations on the systems performance. Finally, conclusions derived from the obtained results are presented in section 5.
URI: http://hdl.handle.net/10553/107508
ISBN: 978-953-307-487-0
DOI: 10.5772/21732
Source: Advanced Biometric Technologies / Girija Chetty, Jucheng Yang (Ed.), p. 225-240
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