Abstract
| Original language | English |
|---|---|
| Title of host publication | 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) |
| Place of Publication | USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 96-103 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781479921263 |
| ISBN (Print) | 9781479921263 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Hobart, TAS, Hobart, Australia Duration: 26 Nov 2013 → 28 Nov 2013 |
Conference
| Conference | 2013 International Conference on Digital Image Computing |
|---|---|
| Country | Australia |
| City | Hobart |
| Period | 26/11/13 → 28/11/13 |
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Biologically significant facial landmarks: How significant are they for gender classification? / Gilani, Syed; Shafait, Faisal; Mian, Ajmal.
2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 96-103 10.1109/DICTA.2013.6691488.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - Biologically significant facial landmarks: How significant are they for gender classification?
AU - Gilani, Syed
AU - Shafait, Faisal
AU - Mian, Ajmal
PY - 2013
Y1 - 2013
N2 - Automatic gender classification has many applications in human computer interaction. However, to determine the gender of an unseen face is challenging because of the diversity and variations in the human face. In this paper, we explore the importance of biologically significant facial landmarks for gender classification and propose a fully automatic gender classification algorithm. We extract 3D Euclidean and Geodesic distances between these landmarks and use feature selection to determine the relative importance of the biological landmarks for classifying gender. Unlike existing techniques, our algorithm is fully automatic since all landmarks are automatically detected. Experiments on one of the largest 3D face databases FRGC v2 show that our algorithm outperforms all existing techniques by a significant margin.
AB - Automatic gender classification has many applications in human computer interaction. However, to determine the gender of an unseen face is challenging because of the diversity and variations in the human face. In this paper, we explore the importance of biologically significant facial landmarks for gender classification and propose a fully automatic gender classification algorithm. We extract 3D Euclidean and Geodesic distances between these landmarks and use feature selection to determine the relative importance of the biological landmarks for classifying gender. Unlike existing techniques, our algorithm is fully automatic since all landmarks are automatically detected. Experiments on one of the largest 3D face databases FRGC v2 show that our algorithm outperforms all existing techniques by a significant margin.
U2 - 10.1109/DICTA.2013.6691488
DO - 10.1109/DICTA.2013.6691488
M3 - Conference paper
SN - 9781479921263
SP - 96
EP - 103
BT - 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - USA
ER -