-
1
-
-
84906907892
-
-
Blender 2.6
-
Blender.org (2013). Blender 2.6.
-
(2013)
Blender.org
-
-
-
2
-
-
0032415354
-
Illumination effects in face recognition
-
Braje, W., Kersten, D., Tarr, M., and Troje, N. (1998). Illumination effects in face recognition. Psychobiology, 26(4):371-380.
-
(1998)
Psychobiology
, vol.26
, Issue.4
, pp. 371-380
-
-
Braje, W.1
Kersten, D.2
Tarr, M.3
Troje, N.4
-
3
-
-
84875461472
-
Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors
-
Chan, C., Tahir, M., Kittler, J., and Pietikainen, M. (2013). Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(5):1164-1177.
-
(2013)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.35
, Issue.5
, pp. 1164-1177
-
-
Chan, C.1
Tahir, M.2
Kittler, J.3
Pietikainen, M.4
-
4
-
-
84887396870
-
Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification
-
Chen, D., Cao, X., Wen, F., and Sun, J. (2013). Blessing of Dimensionality: High-dimensional Feature and Its Efficient Compression for Face Verification. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
-
(2013)
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
-
-
Chen, D.1
Cao, X.2
Wen, F.3
Sun, J.4
-
5
-
-
33645146449
-
Histograms of oriented gradients for human detection
-
IEEE
-
Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886-893. IEEE.
-
(2005)
Computer Vision and Pattern Recognition, 2005. CVPR 2005 IEEE Computer Society Conference on
, vol.1
, pp. 886-893
-
-
Dalal, N.1
Triggs, B.2
-
6
-
-
84856743552
-
How does the brain solve visual object recognition?
-
DiCarlo, J., Zoccolan, D., and Rust, N. (2012). How does the brain solve visual object recognition? Neuron, 73(3):415-434.
-
(2012)
Neuron
, vol.73
, Issue.3
, pp. 415-434
-
-
Dicarlo, J.1
Zoccolan, D.2
Rust, N.3
-
7
-
-
77955422240
-
Object detection with discriminatively trained part-based models
-
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010). Object detection with discriminatively trained part-based models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(9):1627-1645.
-
(2010)
Pattern Analysis and Machine Intelligence IEEE Transactions on
, vol.32
, Issue.9
, pp. 1627-1645
-
-
Felzenszwalb, P.F.1
Girshick, R.B.2
McAllester, D.3
Ramanan, D.4
-
8
-
-
76449115179
-
Multi-pie
-
Gross, R., Matthews, I., Cohn, J., Kanade, T., and Baker, S. (2010). Multi-pie. Image and Vision Computing, 28(5):807-813.
-
(2010)
Image and Vision Computing
, vol.28
, Issue.5
, pp. 807-813
-
-
Gross, R.1
Matthews, I.2
Cohn, J.3
Kanade, T.4
Baker, S.5
-
9
-
-
84856081466
-
Report on the evaluation of 2d still-image face recognition algorithms
-
Grother, P., Quinn, G., and Phillips, P. (2010). Report on the evaluation of 2d still-image face recognition algorithms. NIST Interagency Report, 7709.
-
(2010)
NIST Interagency Report
, vol.7709
-
-
Grother, P.1
Quinn, G.2
Phillips, P.3
-
10
-
-
77953178820
-
Is that you? Metric learning approaches for face identification
-
Kyoto, Japan
-
Guillaumin, M., Verbeek, J., and Chmid, C. (2009). Is that you? Metric learning approaches for face identification. In IEEE International Conference on Computer Vision, pages 498-505, Kyoto, Japan.
-
(2009)
IEEE International Conference on Computer Vision
, pp. 498-505
-
-
Guillaumin, M.1
Verbeek, J.2
Chmid, C.3
-
11
-
-
84887005575
-
Labeled faces in the wild: A database for studying face recognition in unconstrained environments
-
Marseille, Fr
-
Huang, G. B., Mattar, M., Berg, T., and Learned-Miller, E. (2008). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. In Workshop on faces in real-life images: Detection, alignment and recognition (ECCV), Marseille, Fr.
-
(2008)
Workshop on Faces in Real-life Images: Detection, Alignment and Recognition (ECCV)
-
-
Huang, G.B.1
Mattar, M.2
Berg, T.3
Learned-Miller, E.4
-
12
-
-
27644475783
-
Fast readout of object identity frommacaque inferior temporal cortex
-
Hung, C. P., Kreiman, G., Poggio, T., and DiCarlo, J. J. (2005). Fast Readout of Object Identity fromMacaque Inferior Temporal Cortex. Science, 310(5749):863-866.
-
(2005)
Science
, vol.310
, Issue.5749
, pp. 863-866
-
-
Hung, C.P.1
Kreiman, G.2
Poggio, T.3
Dicarlo, J.J.4
-
13
-
-
84898443616
-
Face recognition using local quantized patterns
-
Guildford, UK
-
Hussain, S., Napoléon, T., and Jurie, F. (2012). Face recognition using local quantized patterns. In Proc. British Machine Vision Conference (BMCV), volume 1, pages 52-61, Guildford, UK.
-
(2012)
Proc. British Machine Vision Conference (BMCV)
, vol.1
, pp. 52-61
-
-
Hussain, S.1
Napoléon, T.2
Jurie, F.3
-
14
-
-
84906909063
-
Learning generic invariances in pbject recognition: Translation and scale
-
Leibo, J. Z., Mutch, J., Rosasco, L., Ullman, S., and Poggio, T. (2010). Learning Generic Invariances in Object Recognition: Translation and Scale. MIT-CSAIL-TR-2010-061, CBCL-294.
-
(2010)
MIT-CSAIL-TR-2010-061, CBCL-294
-
-
Leibo, J.Z.1
Mutch, J.2
Rosasco, L.3
Ullman, S.4
Poggio, T.5
-
16
-
-
84878931682
-
CNS: A GPU-based framework for simulating corticallyorganized networks
-
Mutch, J., Knoblich, U., and Poggio, T. (2010). CNS: a GPU-based framework for simulating corticallyorganized networks. MIT-CSAIL-TR, 2010-013(286
-
(2010)
MIT-CSAIL-TR
, vol.2010-2013
, Issue.286
-
-
Mutch, J.1
Knoblich, U.2
Poggio, T.3
-
17
-
-
0036647193
-
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
-
Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971-987.
-
(2002)
Pattern Analysis and Machine Intelligence IEEE Transactions on
, vol.24
, Issue.7
, pp. 971-987
-
-
Ojala, T.1
Pietikainen, M.2
Maenpaa, T.3
-
18
-
-
49049108892
-
Blur insensitive texture classification using local phase quantization
-
Springer
-
Ojansivu, V. and Heikkilä, J. (2008). Blur insensitive texture classification using local phase quantization. In Image and Signal Processing, pages 236-243. Springer.
-
(2008)
Image and Signal Processing
, pp. 236-243
-
-
Ojansivu, V.1
Heikkilä, J.2
-
19
-
-
33745164328
-
Overview of the face recognition grand challenge
-
IEEE
-
Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., and Worek, W. (2005). Overview of the face recognition grand challenge. In Computer vision and pattern recognition, 2005. CVPR 2005. IEEE computer society conference on, volume 1, pages 947-954. IEEE.
-
(2005)
Computer Vision and Pattern Recognition, 2005. CVPR 2005 IEEE Computer Society Conference on
, vol.1
, pp. 947-954
-
-
Phillips, P.J.1
Flynn, P.J.2
Scruggs, T.3
Bowyer, K.W.4
Chang, J.5
Hoffman, K.6
Marques, J.7
Min, J.8
Worek, W.9
-
20
-
-
79952518221
-
Comparing state-of - The-art visual features on invariant object recognition tasks
-
IEEE
-
Pinto, N., Barhomi, Y., Cox, D., and DiCarlo, J. J. (2011). Comparing state-of-the-art visual features on invariant object recognition tasks. In Applications of Computer Vision (WACV), 2011 IEEE Workshop on, pages 463-470. IEEE.
-
(2011)
Applications of Computer Vision (WACV), 2011 IEEE Workshop on
, pp. 463-470
-
-
Pinto, N.1
Barhomi, Y.2
Cox, D.3
Dicarlo, J.J.4
-
21
-
-
38949193299
-
Why is realworld visual object recognition hard?
-
Pinto, N., Cox, D., and DiCarlo, J. J. (2008a). Why is realworld visual object recognition hard? PLoS computational biology, 4(1):e27.
-
(2008)
PLoS Computational Biology
, vol.4
, Issue.1
-
-
Pinto, N.1
Cox, D.2
Dicarlo, J.J.3
-
22
-
-
70450172604
-
How far can you get with a modern face recognition test set using only simple features?
-
IEEE
-
Pinto, N., DiCarlo, J. J., and Cox, D. (2009). How far can you get with a modern face recognition test set using only simple features? In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 2591-2598. IEEE.
-
(2009)
Computer Vision and Pattern Recognition, 2009. CVPR 2009 IEEE Conference on
, pp. 2591-2598
-
-
Pinto, N.1
Dicarlo, J.J.2
Cox, D.3
-
23
-
-
79952501854
-
Establishing good benchmarks and baselines for face recognition
-
Pinto, N., DiCarlo, J. J., Cox, D. D., et al. (2008b). Establishing good benchmarks and baselines for face recognition. In Workshop on Faces in'Real-Life'Images: Detection, Alignment, and Recognition.
-
(2008)
Workshop on Faces In'Real-Life'Images: Detection, Alignment, and Recognition
-
-
Pinto, N.1
Dicarlo, J.J.2
Cox, D.D.3
-
24
-
-
84897571792
-
The computational magic of the ventral stream: Sketch of a theory (and why some deep architectures work
-
Poggio, T., Mutch, J., Anselmi, F., Leibo, J. Z., Rosasco, L., and Tacchetti, A. (2012). The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). MIT-CSAIL-TR-2012-035.
-
(2012)
MIT-CSAIL-TR-2012-035
-
-
Poggio, T.1
Mutch, J.2
Anselmi, F.3
Leibo, J.Z.4
Rosasco, L.5
Tacchetti, A.6
-
25
-
-
0033316361
-
Hierarchical models of object recognition in cortex
-
Riesenhuber, M. and Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11):1019-1025.
-
(1999)
Nature Neuroscience
, vol.2
, Issue.11
, pp. 1019-1025
-
-
Riesenhuber, M.1
Poggio, T.2
-
26
-
-
34247096930
-
A feedforward architecture accounts for rapid categorization
-
Serre, T., Oliva, A., and Poggio, T. (2007). A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences of the United States of America, 104(15):6424-6429.
-
(2007)
Proceedings of the National Academy of Sciences of the United States of America
, vol.104
, Issue.15
, pp. 6424-6429
-
-
Serre, T.1
Oliva, A.2
Poggio, T.3
-
27
-
-
84906912661
-
-
FaceGen Modeller
-
Singular Inversions (2003). FaceGen Modeller 3.
-
(2003)
Singular Inversions
, pp. 3
-
-
-
28
-
-
0342561506
-
Face recognition under varying poses: The role of texture and shape
-
Troje, N. and Bülthoff, H. (1996). Face recognition under varying poses: The role of texture and shape. Vision Research, 36(12):1761-1771.
-
(1996)
Vision Research
, vol.36
, Issue.12
, pp. 1761-1771
-
-
Troje, N.1
Bülthoff, H.2
-
29
-
-
78951492346
-
Empowering visual categorization with the gpu
-
van de Sande, K. E. A., Gevers, T., and Snoek, C. G. M. (2011). Empowering visual categorization with the gpu. IEEE Transactions on Multimedia, 13(1):60-70.
-
(2011)
IEEE Transactions on Multimedia
, vol.13
, Issue.1
, pp. 60-70
-
-
Van De Sande, K.E.A.1
Gevers, T.2
Snoek, C.G.M.3
-
32
-
-
80051961576
-
Effective unconstrained face recognition by combining multiple descriptors and learned background statistics
-
Wolf, L., Hassner, T., and Taigman, Y. (2011). Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):1978-1990.
-
(2011)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.33
, Issue.10
, pp. 1978-1990
-
-
Wolf, L.1
Hassner, T.2
Taigman, Y.3
-
33
-
-
84866667680
-
Face detection, pose estimation, and landmark localization in the wild
-
Providence, RI
-
Zhu, X. and Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pages 2879-2886, Providence, RI.
-
(2012)
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
, pp. 2879-2886
-
-
Zhu, X.1
Ramanan, D.2
|