face recognition demo

8/18/2019 Face Recognition Demo

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FACE RECOGNITION

PROJECT


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Outline

1. Face image cropping and preprocessing

2. Appearance-based face recognition methods•

Principal Component Analysis (PCA)• Linear Discriminant Analysis (LDA)

• Locality Preserving Projections (LPP)

• K-nearest neighbor as the classification method

3. Experimental Results

4. Demonstration


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Face image cropping and preprocessing

• Face Detection with OpenCV Library

• Normalization

• Cropping

• Grayscale conversion

• Fixing of locations of eyes (by OpenCV)

• Fixing to the same resolution

• Preprocessing

• Histogram Equalization


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Our Dataset


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Appearance-based methods

• PCA

• dimensionality reduction method

• produces a compact representation

• LDA

• supervised learning algorithm

• find a subspace which separate different classes of objects

• LPP• considers the manifold structure for face analysis


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1. Training

2. Recognizing

Reference: [1]


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Eigenfaces

Fisherfaces

Laplacianfaces

Eigenfaces, Fisherfaces & Laplacianfaces

PCA

LDA

LPP


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PCA Reconstruction

Original Face d = 2 d = 6 d = 10 d = 20 d = 40 d =60

Original Face d = 2 d = 6 d = 10 d = 20 d = 40 d =60


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Experimental Results

1. PCA with different energy percentage

2. Different metric types

3. Different partitions for validation

4. Different K for K-nearest neighbor

5. LPP with different PCA processing


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PCA with different energy percentage

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.5 0.6 0.7 0.8 0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99

own dataset

ORL dataset

A c c u r a c y

Energy Percentage


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Different metric types

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Cosine Dissimilarity L1 Distance Euclidean Distance

PCA

LDA

LPP

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Cosinedissimilarity

L1 distance Euclideandistance

PCA

LDA

LPP

ORL Dataset Our Dataset

Accuracy Accuracy


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Different partitions for validation

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

3 4 5

PCA

LDA

LPP

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

3 4 5

PCA

LDA

LPP

Training NumberTraining Number

Accuracy Accuracy



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Different K for K-nearest neighbor

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3

PCA

LDA

LPP

KNN_K0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1 2 3

PCA

LDA

LPP

KNN_K

Accuracy Accuracy



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LPP with different PCA processing

0.7

0.75

0.8

0.85

0.9

0.95

40 60 80 100

LPP

PCA

Number of

Components

Accuracy


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Conclusion

• PCA performs the best on ORL dataset

• LDA performs the best on our dataset

•

Out dataset includes lots of variations

• LPP does not perform as expected

• This method still could achieve good results with proper setting

• A more complex method calculating weight matrix may help


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References

[1] Delac, K., Grgic, M., & Grgic, S. (2005). Independent comparative study ofPCA, ICA, and LDA on the FERET data set. International Journal of ImagingSystems and Technology , 15 (5), 252-260.

[2] Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal ofcognitive neuroscience, 3(1), 71-86.

[3] Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs.fisherfaces: Recognition using class specific linear projection. Pattern Analysisand Machine Intelligence, IEEE Transactions on, 19(7), 711-720.

[4] He, X., Yan, S., Hu, Y., Niyogi, P., & Zhang, H. J. (2005). Face recognitionusing laplacianfaces. Pattern Analysis and Machine Intelligence, IEEETransactions on, 27 (3), 328-340.

[5] Viola, P., & Jones, M. J. (2004). Robust real-time face detection.International journal of computer vision, 57 (2), 137-154.

face recognition demo

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