Enhancing Gender Classification by Combining 3D and 2D Face Modalities

Xia, Baiqiang; Ben Amor, Boulbaba; Di, Huang; Mohamed, Daoudi; Yunhong, Wang; Drira, Hassen

Document type :

Communication dans un congrès avec actes

Title :

Enhancing Gender Classification by Combining 3D and 2D Face Modalities

Author(s) :

Xia, Baiqiang [Auteur]
FOX MIIRE [LIFL]
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Di, Huang [Auteur]
School of Mathematics and Systems Science
Mohamed, Daoudi [Auteur]
FOX MIIRE [LIFL]
Yunhong, Wang [Auteur]
School of Mathematics and Systems Science
Drira, Hassen [Auteur]

FOX MIIRE [LIFL]

Conference title :

21th European Signal Processing Conference (EUSIPCO)

Country :

Maroc

Start date of the conference :

2013-09-09

Book title :

Proceeding of European Signal Processing Conference (EUSIPCO)

Publication date :

2013-09-09

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

English abstract : [en]

Shape and texture provide different modalities in face-based gender classification. Although extensive works have been reported in the literature, the majority of them are in the scope of shape or texture modality individually. ...
Show more >Shape and texture provide different modalities in face-based gender classification. Although extensive works have been reported in the literature, the majority of them are in the scope of shape or texture modality individually. Among them, only a few concern their combination, and to the best of our knowledge, no work considers the combination with the 3D face surface. In our work, we investigate the combination of shape and texture modalities for gender classification, with both the combination of range images and gray images, and the combination of 3D meshes and gray images. In 10-fold subject-independent cross-validation with Random Forest on the FRGC-2.0 dataset, we achieved a correct gender classification rate of 93.27%± 5.16, which outperforms each individual modality and is comparable to the state-of-the-art. Results confirm that shape and texture modalities are complementary, and their combination enhances the performance of face-based gender classification.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :