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Face Identification for Humanoid Robot

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< Xin Wang > Face-ID recognition module for OPASCA Karlsruhe, < 2nd Sep. 2008 >
Outline for the presentation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Task und system Requirement ◆ Task : Implementation of an optical face-ID recognition module using MATLAB with steering camera in real-time application ◆ Requirement : Good recognition rate with robustness property Multi-person , Multi-places , Multi-illumination , Cross-poses , Cross-expressions , Cross-view of angle As the real situations the social robot will encounter ! ?
Outline for the presentation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Project Overview ◆ System diagram : Face Detection Filtering Subspace Projection Recognition System ID
Implementation steps ,[object Object],Face Data-base Creation Eigenface Fisherface Filters (Gabor, Histogram) Classifier (N-Nearest, Mahalanobis) Composition Training Set Core Algorithm Improvement Solution Train &Test Image
Outline for the presentation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object]
Outline for the presentation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],M=
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],M eigenvectors correspoding to M largest non-zero eigenvalues
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],Test Matched Image:
Implementation steps ,[object Object],[object Object],Remark: the testing is based on the following condition , with 6-9 persons in each test for both training set and testing pictures, and 50 pictures for each person in the test and training set is assumed
Outline for the presentation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],[object Object],[object Object],?
Implementation steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Where: S w and S B are Both N × N size and c :number of class, m :mean of all image m i : mean of class i th n i : No. of images in i th class
Implementation steps ,[object Object],[object Object],[object Object],Finding an optimal linear mapping W
Implementation steps ,[object Object],[object Object],[object Object]
Implementation steps ,[object Object],[object Object],Eigen:Test2 Room1,Fisher Test6 Room1
Outline for the presentation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Improvement steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Exploit salient visual properties, spatial localization,orientation selectivity as retina
Improvement steps ,[object Object],[object Object],[object Object],[object Object],5 scales,8 Phase , Real part
Improvement steps ,[object Object],[object Object],[object Object],[object Object],[object Object],After Gabor After Histeq
Improvement steps ◆ Gabor-wavelet filter for preprocessing Testing from Room5
Outline for the presentation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Improvement steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],TEST 13, ROOM1
Improvement steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline for the presentation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Improvement steps ,[object Object],Test13, Room1 Result: 3%-5% improvement for avg and std
Project Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Future work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Q&A ,[object Object],[object Object]
Additional ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Additional ,[object Object],[object Object],[object Object],[object Object],[object Object],Test15, 120 pic in training
Additional ,[object Object],?
Additional ,[object Object]
Additional ,[object Object]
Additional ,[object Object],[object Object]
Additional ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
<Subtopic> <Title> (P-C) × 1 Vector (C-1) × 1 Vector PCA LDA N×1 Vector X matrix

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Face Identification for Humanoid Robot

  • 1. < Xin Wang > Face-ID recognition module for OPASCA Karlsruhe, < 2nd Sep. 2008 >
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  • 3. Task und system Requirement ◆ Task : Implementation of an optical face-ID recognition module using MATLAB with steering camera in real-time application ◆ Requirement : Good recognition rate with robustness property Multi-person , Multi-places , Multi-illumination , Cross-poses , Cross-expressions , Cross-view of angle As the real situations the social robot will encounter ! ?
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  • 5. Project Overview ◆ System diagram : Face Detection Filtering Subspace Projection Recognition System ID
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  • 32. Improvement steps ◆ Gabor-wavelet filter for preprocessing Testing from Room5
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  • 48. <Subtopic> <Title> (P-C) × 1 Vector (C-1) × 1 Vector PCA LDA N×1 Vector X matrix