The document discusses various methodologies and datasets related to face recognition using deep learning, highlighting prominent datasets such as ORL, FERET, and LFW. It compares the accuracy of different face recognition algorithms, including DeepFace and DeepID, while stressing the importance of dataset size and face alignment for improved accuracy. Recommendations for optimizing face recognition processes are provided, focusing on architecture and joint identification-verification techniques.

1. Face Recognition Based
on Deep Learning
recognizzit
www.recognizz.it

2. Face recognition approaches
Verification
Identification
Similarity
Attributes

3. Benchmarks
ORL
FERET
Labeled Faces in the Wild (LFW)
YouTube Faces (YTF)

4. ORL
Images taken between
April 1992 and April 1994
at the lab
There are 10 different
images of each of 40
distinct subjects
The size of each image
is 92x112 pixels, with 256
grey levels per pixel
~10 mb
* http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

5. FERET
Images taken in 2 steps:
1993-1997
1998-2001
1200 subjects,
14051 face images
fa fb dublicate 1 fc dublicate 2
Heads with views ranging from
frontal to left and right profiles
Different lightning
Size ~ 8GB
* http://www.itl.nist.gov/iad/humanid/feret/feret_master.html

6. Labeled Faces in the Wild
Faces collected
from the web
1680 subjects,
13 000 face images
Size ~200mb
* http://vis-www.cs.umass.edu/lfw/

7. LFW unrestricted with labeled outside
data protocol
10 batches
Each batch:
300 matched
300 mismatched
Huang, G.B., Learned-Miller, E.: Labeled faces in the wild: Updates and new reporting procedures.
Technical Report UM-CS-2014-003, UMass Amherst (2014)
Round 1
Validation Set
Training Set
Round 2 Round 3 Round 10
...

8. Face Recognition Algorithm
Face localization Normalization Feature extraction Comparing
Verification
metric
FALSE

9. What was before DCN
Method Accuracy ± SE
combined Joint Bayesian 0.9242 ±0.0108
Tom-vs-Pete + Attribute 0.9330 ±0.0128
High-dim LBP 0.9517 ±0.0113
TL Joint Bayesian 0.9633 ± 0.0108
Human, cropped 0.975

10. DeepFace
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance
in face verification. In CVPR, 2014

11. Training data
Dataset: Social Face
Classification (4030
subjects, ~4.4 million
face images)
Alignment:
2D
3D

12. Architecture
Input: RGB image 152x152
Output feature size: 4096
Parameters: ~ 120 million
Verification metric:
weighted chi-squared distance
siamese

13. Results on LFW
Method Accuracy ± SE
combined Joint Bayesian 0.9242 ±0.0108
Tom-vs-Pete + Attribute 0.9330 ±0.0128
High-dim LBP 0.9517 ±0.0113
TL Joint Bayesian 0.9633 ± 0.0108
DeepFace-align2D 0.943 ± 0.0043
DeepFace-Siamese 0.9617 ± 0.0038
DeepFace-ensemble 0.9735 ± 0.0025
Human, cropped 0.975

14. DeepID
Y. Sun, X. Wang, and X. Tang. Deep learning face representation from predicting 10,000 classes. In CVPR, 2014.

15. Training data
Dataset: CelebFaces
(10 177 subjects,
202 599 face images)
Alignment:
2D
Patch

16. Architecture
Input: 39x31 RGB or grayscale
Output feature size: 160
Additional algorithms:
Joint Bayesian
PCAONLY 1 PATCH

17. Results on LFW
Method Accuracy ± SE#Net
DeepFace-Siamese 1
7
60
100
100
0.9617 ± 0.0038
DeepFace-ensemble 0.9735 ± 0.0025
DeepID on CelebFaces 0.9605 ± …
DeepID on CelebFaces+ 0.972 ± …
DeepID on CelebFaces+ TL 0.9745 ± 0.0026
Human, cropped 0.975

18. CASIA
D. Yi, Z. Lei, S. Liao, and S. Z. Li. Learning face representation from scratch. Technical report, arXiv:1411.7923, 2014.

19. Training data
Dataset: CASIA WebFace
(10 575 subjects and
494 414 face images)
Alignment: 2D

20. Architecture
Input: gray image 100x100
Output feature vector size: 320
Parameters:~ 5 million
Verification metric: Cosine
Additional algorithms:
Joint Bayes
PCA
Combined identification
and verification loss
Very deep architecture
Face Labels
Cost2
Contrastive
Verification Labels
[0,1]
Cost1
Softmax
Conv12
Conv11
Poo11
2x2+2(S)
Conv22
Conv21
Poo12
2x2+2(S)
Conv32
Conv31
Poo13
2x2+2(S)
Fc6
Dropcut
40%
Poo15
7x7+1(S)
Conv51
Conv52
Poo14
2x2+2(S)
Conv41
Conv42

21. Results on LFW
Method Accuracy ± SE#Net
DeepFace 7
100
1
1
1
1
1
0.9735 ± 0.0025
DeepID 0.9745 ± 0.0026
DR + Cosine 0.9613 ± 0.003
DR + PCA on CASIA-WebFace + Cosine 0.963 ± 0.0035
DR + Joint Bayes on CASIA-WebFace 0.973 ± 0.0031
DR + PCA on LFW training set + Cosine
DR + Joint Bayse on LFW training set
0.9633 ± 0.0042
0.9773 ± 0.0031
* DR – CASIA-WebFace

22. CASIA replication
Training data Accuracy on LFW
normalized 97,27 %
no alignment 94 %
our alignment 96,2 %

23. Database size
Jingtuo Liu, Yafeng Deng, Tao Bai, Zhengping Wei, and Chang Huang; Baidu�Targeting Ultimate Accuracy:
Face Recognition via Deep Embedding.
Identities Error rate
1.5K 3.1%
9K 1.35%
18K
Faces
150K
450K
1.2M 0.87%

24. Database availability
Dataset #Images#Subjects
LFW 5 749
2 995
10 177
4 030
2 000
10 575
13 233
WDRef 99 773
CelebFaces 202 599
SFC 4 400 000
CACD 163 446
CASIA-WebFace 494 414
Availability
Public
Public (feature only)
Private
Private
Public
(partial annotated)
Public
D. Yi, Z. Lei, S. Liao, and S. Z. Li. Learning face representation from scratch.
Technical report, arXiv:1411.7923, 2014.

25. Recommendations
Big dataset with large amount of subjects
Careful face extraction and alignment
Deep architecture
Joint Identification-Verification
K. Simonyan and A. Zisserman. “Very deep convolutional networks for large-scale image recognition”.
arXiv preprint arXiv:1409.1556, 2014
Y. Sun, Y. Chen, X. Wang, and X. Tang. Deep learning face representation by joint identification-verification. In NIPS, 2014.

26. THANK YOU
Yurii Pashchenko
george.pashchenko@gmail.com
www.recognizz.it