Train a Classifier Based on the Huge Face Database Presented by: Jie Chen Jie Chen, Ruiping Wang, Shengye Yan, Shiguang Shan, Xilin Chen, Wen Gao.

Train a Classifier Based on the Huge Face

Database

Presented by: Jie Chen

Jie Chen, Ruiping Wang, Shengye Yan, Shiguang Shan, Xilin Chen, Wen Gao

Motivation Data collection is tedious but essential for

learning based algorithms In Viola CVPR 2001, bootstrap for negative; Ours: Resampling the positive set, besides the

bootstrap for negative.

Why? Collected face samples randomly;

Result in the bias of the trained detector.

How? Fill in the face example space by GA; Subsample it by manifold; Mend by SVM.

The collected face and nonface

set

nonface set

face set

nonface set

face set

Resulting distribution.

Contribution of this Paper Subsample a small but efficient and

representative subset based on the manifold: Discuss the effects of outliers; The performance is instable to train a detector based

on the random subsampling. However, a detector trained on the subsampled face set by manifold is not only stable but also performance improved;

When we prepare the training set, we should collect more samples along those dimensionalities with larger variances to get a nearly uniformed distribution in the manifold, for example, left-right pose of faces more than up-down pose.

A typical manifold – Swiss Roll(B. J. Tenenbaum, V. Silva, and J. Langford )

Manifold

from http://www.cs.toronto.edu/~roweis/lle/

Face Sample Manifold

An individual with varying pose and expression

from http://www.cs.toronto.edu/~roweis/lle/

Too dense!Too sparse!

Dimensionalities of Isomap The residual variance of Isomap

embedding on the 698 face database

lighting direction

up-down pose

left-right pose

Dimensionalities Each coordinate axis of the embedding correlates

highly with one degree of freedom underlying the original data:

left-right pose corresponding to the first degree of freedom;

up-down pose corresponding to the second one ; lighting direction to the third one.

That is to say the scatter of face images in left-right pose is the biggest while the scatter in lighting is the smallest among these three factors.

We conclude that, in order to select representative example set, we should pay more attention to the left-right pose variations than the up-down pose.

Subsampling by manifold

1

3

2

(a) illustration of subsampling based on the estimated geodesic distance; (b) manifold of 698 faces; (c) subsampled results.

(a) (b) (c)

Experiments:Subsampling by manifold training set -- 6,977 images (2,429 faces

and 4,548 non-faces) testing set -- 24,045 images (472 faces

and 23,573 non-faces). All of these images are grayscale and

they are available on the CBCL webpage. let K=6 for the manifold learning. Trained on the AdaBoost based classifier

Subsampling based on manifold

Some possible reasons: Examples subsampled based on the manifold distribute r

easonable in the example space and have no example congregating compared with the whole set;

Outliers in the whole set deteriorate its performance

Subsampling based on the manifold and random

Results based on random subsampling is much instable

Outliers effects

Outliers deteriorate its performance

Large scale of database The face-image database consists of 100,000

faces (collected form web, video and digital camera);

Randomly rotate , translate and scale; After these preprocessing, we get 1,200,000 face

images which constitute the whole set; The first group is composed of 15,000 face

images which are subsampled by the manifold (ISO15000) ;

The second or third group is also composed of 15,000 face images which are random subsampling (Rand1-15000 and Rand2-15000).

Test on MIT+CMU set Sampled training set by the manifold and

the random subsampled set Trained on the AdaBoost based classifier

The ROC curves comparison

Compared with other published algorithms on the MIT+CMU face test set

Conclusion Present a manifold-based method to subsample.

Compared with the detector by random subsampling, the detector trained by manifold is more stable and achieve better performance.

Improved performance results from: Reasonable-distributed examples, subsampled based on

manifold, No outliers, discarded during the manifold learning

Some outputs of our detector

Thank you very much!

By the way…

1. Demo outside Face Recognition against a large scale

face database from our lab. 2. BJUT-3D face database available

500 3D faces! Free! Assign a release agreement For research purpose only

Get it now outside beside the demo desk.