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

1. A Face processing system Based on Committee Machine: The Approach and Experimental Results Presented by: Harvest Jang 29 Jan 2003

2. Outline • Introduction • Background • Face processing system • System Architecture • Face Detection Committee Machine • Face Recognition Committee Machine • Experimental result • Conclusion and Future work

3. 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

4. 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

5. Face processing system • Three main components • Pre-processing • Face Detection Committee Machine (FDCM) • Face Recognition Committee Machine (FRCM) Fig 1: System architecture

6. Pre-processing • Transform to YCrCb color space • Use ellipse color model to locate the flesh color • Perform morphological operation to reduce noise • 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

7. Transform to various scale Histogram equalization Apply a 19x19 search window 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 Fig 4: Face detection step

8. 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

9. 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

10. 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

11. 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

12. Face Recognition Committee Machine • Mixture of five experts Fig 7: System architecture for FRCM

13. 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

14. 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

15. FRCM – Problem modeling (2) • 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

16. 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

17. 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

18. ORL Face Database 40 people 10 images/person Yale Face Database 15 people 11 images/person Experimental result - FRCM

19. Experimental result - FRCM • ORL Face database

20. Experimental result - FRCM • Yale Face Database

21. 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

22. Thank you!