A Face processing system Based on Committee Machine: The Approach and Experimental Results

Presented by: Harvest Jang

29 Jan 2003

Outline Introduction Background Face processing system

System Architecture Face Detection Committee Machine Face Recognition Committee Machine

Experimental result Conclusion and Future work

Introduction Information retrieval from biometric technology

become popular Human face is one of the input source that can get

easily for further processing A wide range of usage for face processing system,

for example, Person identification system Video content-based information retrieval Security entrance system

Background Homogenous committee machine

Train experts by different training data sets to arrive a union decision

For example Ensemble of networks Gating network Mixture of experts (neural networks or RBF)

We propose a heterogeneous committee machine for face processing Train different classifiers from different approaches to make the

final decision Capture more features in the same training data Overcome the inadequacy of each single approach

Face processing system Three main components

Pre-processing Face Detection Committee Machine (FDCM) Face Recognition Committee Machine (FRCM)

Fig 1: System architecture

Pre-processing1. Transform to YCrCb color space

2. Use ellipse color model to locate the flesh color

3. Perform morphological operation to reduce noise

4. Skin segmentation to find face candidates

Fig 2: 2D projection in the CrCb subspace (gray dots represent skin color samples and black dots represent non-skin tone color)

Fig 3: Pre-processing step (a) original images, (b) binary skin mask, (c) binary skin mask after morphological operation and (d) Face candidates

Pre-processing To detect different size of faces, the region is resized to various scales A 19x19 search window is searching around the re-sized regions Histogram equalization is performed to the search window

Histogram equalization

Transform to various scale

Apply a 19x19 search window

Fig 4: Face detection step

Face Detection Committee Machine Compose of three approaches

Neural network Sparse Network of Winnow (SNoW) Support vector machine (SVM)

Fig 5: System architecture for FDCM

FDCM – Problem modeling (1) Based-on the confidence value of

each expert iiT

Fig 6: The distribution of confident value of the training data from three different approaches

FDCM – Problem modeling (2) The confidence value of each expert are

Not uniform function Not fixed output range (e.g. –1 to 1 or 0 to 1)

Normalization is required using statistics information getting from the training data

where is the mean value of training face pattern from expert i and is the standard derivation of training data from expert i

iiii T /)( i

i

FDCM – Problem modeling (3) The information of the confidence value

from experts can be preserved The output value of the committee machine

can be calculated:

where is the criteria factor for expert i and is the weight of the expert i

i

iiiiw )*(*

i iw

Face Recognition Committee Machine Mixture of five experts

Fig 7: System architecture for FRCM

FRCM Result r(i)

Individual expert’s result for test image Confidence c(i)

How confident the expert on the result Weight w(i)

Average performance of an expert

FRCM – Problem modeling (1) Eigenface, Fisherface, EGM

K nearest-neighbor classifiers SVM

One-against-one approach used For J different classes, J(J-1)/2 SVM are constructed

Result value:

Confidence value:where c(i) is the confidence value for expert i, r(i) is the result of the expert i and v() is the highest votes in class j

Neural network Result value:

Class with output value closest to 1 Confidence value:

Output value

Score function:

where c(i) is the confidence value for expert i and w(i) is the weight of the expert i

FRCM – Problem modeling (2)

Experimental result - FDCM CBCL face database from MIT

Training set (2429 face pattern, 4548 non-face pattern with 19x19 pixel)

Testing set (472 face pattern, 23573 non-face pattern with 19x19 pixel)

Table 1: experimental results on images from the testing set of CBCL database

Experimental result - FDCM To better represent the detectability of each model, ROC curve

instead of single point of criterion response

Fig 8 The ROC curves of committee machine and three different approaches

Experimental result - FRCM ORL Face Database

40 people 10 images/person

Yale Face Database 15 people 11 images/person

Experimental result - FRCM ORL Face database

Experimental result - FRCM Yale Face Database

Conclusion and Future work We propose a heterogeneous committee machine

approaches for face processing Face Detection Committee Machine (FDCM) Face Recognition Committee Machine (FRCM) Combine the state-of-the-art approaches Improve in accuracy and experimental results are satisfactory

We have implemented a real-time face processing system Can detect and tracking the face automatically Work well for upright frontal face in varies lighting conditions

We may use other biometric module such as fingerprint and hand geometry to improve the accuracy of the system

Thank you!