-
1
-
-
0027343823
-
Sex discrimination: How do we tell the difference between male and female faces?
-
V. Bruce, A. M. Burton, N. Dench, E. Hanna, P. Healey, O. Mason, A. Coombes, R. Fright, and A. Linney. Sex discrimination: How do we tell the difference between male and female faces? Perception, 22:131-152, 1993.
-
(1993)
Perception
, vol.22
, pp. 131-152
-
-
Bruce, V.1
Burton, A.M.2
Dench, N.3
Hanna, E.4
Healey, P.5
Mason, O.6
Coombes, A.7
Fright, R.8
Linney, A.9
-
4
-
-
0027352107
-
What's the difference between men and women? Evidence from facial measurement
-
A. M. Burton, V. Bruce, and N. Dench. What's the difference between men and women? evidence from facial measurement. Perception, 22:153-176, 1993.
-
(1993)
Perception
, vol.22
, pp. 153-176
-
-
Burton, A.M.1
Bruce, V.2
Dench, N.3
-
8
-
-
0001203164
-
Sex classification of face areas: How well can a linear neural network predict human performance
-
B. Edelman, D. Valentin, and H. Abdi. Sex classification of face areas: how well can a linear neural network predict human performance. Journal of Biological System, 6(3):241-264, 1998.
-
(1998)
Journal of Biological System
, vol.6
, Issue.3
, pp. 241-264
-
-
Edelman, B.1
Valentin, D.2
Abdi, H.3
-
9
-
-
0003798631
-
A unified framework for regularization networks and support vector machines
-
MIT
-
Theodoros Evgeniou, Massimiliano Pontil, and Tomaso Poggio. A unified framework for regularization networks and support vector machines. Technical Report AI Memo No. 1654, MIT, 1999.
-
(1999)
Technical Report AI Memo No. 1654
-
-
Evgeniou, T.1
Pontil, M.2
Poggio, T.3
-
15
-
-
0030632418
-
Sex classification is better with three-dimensional structure than with image intensity information
-
A. J. O'Toole, T. Vetter, N. F. Troje, and H. H. Bulthoff. Sex classification is better with three-dimensional structure than with image intensity information. Perception, 26:75-84, 1997.
-
(1997)
Perception
, vol.26
, pp. 75-84
-
-
O'Toole, A.J.1
Vetter, T.2
Troje, N.F.3
Bulthoff, H.H.4
-
16
-
-
84898985444
-
Support vector machines applied to face recognition
-
M. S. Kearns, S. Solla, and D. Cohen, editors MIT Press
-
P. J. Phillips. Support vector machines applied to face recognition. In M. S. Kearns, S. Solla, and D. Cohen, editors, Advances in Neural Information Processing Systems 11, Volume 11, pages 803-809. MIT Press, 1998.
-
(1998)
Advances in Neural Information Processing Systems 11
, vol.11
, pp. 803-809
-
-
Phillips, P.J.1
-
17
-
-
0025490985
-
Networks for approximation and learning
-
T. Poggio and F. Girosi. Networks for approximation and learning. Proceedings of the IEEE, 78(9):1481-1497, 1990.
-
(1990)
Proceedings of the IEEE
, vol.78
, Issue.9
, pp. 1481-1497
-
-
Poggio, T.1
Girosi, F.2
-
18
-
-
0030081133
-
Male/female identification from 8×6 very low resolution face images by neural network
-
S. Tamura, H. Kawai, and H. Mitsumoto. Male/female identification from 8×6 very low resolution face images by neural network. Pattern Recognition, 29(2):331-335, 1996.
-
(1996)
Pattern Recognition
, vol.29
, Issue.2
, pp. 331-335
-
-
Tamura, S.1
Kawai, H.2
Mitsumoto, H.3
-
20
-
-
0002864738
-
Face recognition and gender determination
-
Laurenz Wiskott, Jean-Marc Fellous, Norbert Krüger, and Christoph von der Malsburg. Face recognition and gender determination. In Proceedings of the International Workshop on Automatic Face and Gesture Recognition, pages 92-97, 1995.
-
(1995)
Proceedings of the International Workshop on Automatic Face and Gesture Recognition
, pp. 92-97
-
-
Wiskott, L.1
Fellous, J.-M.2
Krüger, N.3
Von Der Malsburg, C.4
|