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-processing 1. 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 i 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

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: whereis the criteria factor for expert i and is the weight of the expert i

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, 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!