Using CMU pie human face database to a convolutional neural network - neocognitron

ESANN 2005 Proceedings - 13th European Symposium on Artificial Neural Networks

Volumn , Issue , 2007, Pages 491-496

  Saito, José Hiroki ^a   de Carvalho, Tiago Vieira ^a   Hirakuri, Marcelo ^a   Saunite, André ^a   Ide, Alessandro Noriaki ^b   Abib, Sandra ^a  

^a FEDERAL UNIVERSITY OF SÃO CARLOS   (Brazil)

^b UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVATION THRESHOLDS; BEST VALUE; CONVOLUTIONAL NEURAL NETWORK; HUMAN FACE RECOGNITION; HUMAN FACES; NEOCOGNITRON; OPTIMAL THRESHOLD; TRAINING IMAGE;

DATABASE SYSTEMS; EXPERIMENTS; FACE RECOGNITION;

NEURAL NETWORKS;

EID: 84887006342     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Yann LeCun and Yoshua Bengio. Convolutional Networks for Images, Speech, and Time Series. The Handbook of Brain Theory and Neural Networks, M.Arbib, Ed. Cambridge, MA: MIT Press, 2nd ed., pages 276-279, 2003.
2. Sim, T.; Baker, S.; Bsat, M. - The CMU Pose, Illumination, and Expression (PIE) Database of Human Faces, CMU-RI-TR-01-02, pages. 1-17, USA, 2002.
3. Fukushima, K. Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position. Biological Cybernetics, 36, pages. 193-202,1 1980.
4. ETL1 database: http://www.etl.go.jp/~etlcdb/index.html
5. Fukushima, K. Neocognitron for Handwritten Digit Recognition, Neurocomputing, V.51, pages 161-180, 2003.
6. Neubauer, C. Evaluation of Convolutional Neural Networks for Visual Recognition, IEEE Transactions on Neural Networks, Vol. 9, n.4, pages 685-696, 1998.
7. Fukushima, K.; Wake, N. Improved Neocognitron with Bend Detecting Cells, International Joint Conference on Neural Networks, pages 190-195, Baltimore-Maryland, 1992.