1. Face Recognition based on 2D-PCA and CNN
hongliang xue

2. Motivation Face recognition technology is widely used in our lives
Using MATLAB
ORL database

3. 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.

4. 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 eigenvectors

5. 2D-PCA Train_num(per class) Test_num(per class) d Accuracy(%) Time(s)3 7 8
85.36%
1.76 5
91.5%
1.806 9 1
95%
1.582 2
90%
1.525 16
92.5%
1.64 32
1.86

6. 2D-PCA

7. CNN

8. CNN using deepLearnToolbox-master written by Rasmus Berg Palm
still tuning parameters
get about 15% error rate using model of 2 layers of convolution

9. 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 dataset

10. 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 , 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 , January 1997 3.
4. Lec 16 Deep Neural Network (2), Yu Hen Hu

11. Questions