Gender estimation based on deep learned and handcrafted features in an uncontrolled environment
Multimedia Systems, ISSN: 1432-1882, Vol: 29, Issue: 1, Page: 421-433
2023
- 5Citations
- 2Captures
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Article Description
Automatic gender estimation provides a valuable information in the face of analysis tasks and has been widely used in many fields of applications like human–computer interaction, biometrics, video surveillance, activity recognition. This paper introduces a new facial gender estimation method based on a hybrid architecture which combines deep learned features as global information and handcrafted features as local information. A Min Redundancy Max Relevance algorithm was applied to select the highest relevant and lowest redundant features. Such process provides a good compromise in terms of speed and accuracy rate. The experimental study was conducted on the Image Of Groups and FERET databases. The obtained results proved the efficiency of the proposed method in dealing with the facial gender estimation task in an uncontrolled environment.
Bibliographic Details
Springer Science and Business Media LLC
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