hongliang xue Motivation Face recognition technology is widely used in our lives Using MATLAB ORL database Database The ORL Database of Faces taken between April 1992 and April 1994 at the Cambridge University Computer ID: 568579
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Slide1
Face Recognition based on 2D-PCA and CNN
hongliang
xueSlide2
Motivation
Face recognition technology is widely used in our lives
Using MATLAB
ORL databaseSlide3
Database
The ORL Database of Faces
taken between April 1992 and April 1994 at the Cambridge University Computer
Laboratory
10 different images of each of 40 distinct subjects.
http://
www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.htmlSlide4
2D-PCA
Conventional 1D-PCA: transform image matrix to 1D vector
2D-PCA: use image matrix to form a covariance matrix
easier to determine corresponding eigenvectorsSlide5
2D-PCA
Train_num
(per class)
Test_num
(per class)
d
Accuracy(%)
Time(s)
3
7885.36%1.7655891.5%1.80691895%1.58291290%1.525911692.5%1.64913292.5%1.86Slide6
2D-PCASlide7
CNNSlide8
CNN
using
deepLearnToolbox
-master written by
Rasmus
Berg
Palm
https://github.com/rasmusbergpalm/DeepLearnToolbox
still tuning parameters get about 15% error rate using model of 2 layers of convolutionSlide9
Conclusion
2D-PCA:
simple algorithm, accuracy quite high ( 90~95% )
may not perform well for larger dataset
CNN:
hard to find optimal parameters, takes a lot of time can perform well for large datasetSlide10
References
1. Jian
Yang; Zhang, D.;
Frangi
, A.F.; Jing-Yu Yang, “Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition”, in
Pattern Analysis and Machine Intelligence, IEEE Transactions on
, vol. 26, no. 1, pp. 131-137, January 2004
2. Lawrence
, S.; Giles, C.L.; Tsoi, A.C.; Back, A.D. “Face Recognition: A Convolutional Neural-Network Approach”, in Neural Networks, IEEE Transactions on, vol. 8, no. 1, pp. 98-113, January 1997
3. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html4. Lec 16 Deep Neural Network (2), Yu Hen HuSlide11
Questions