Face Identification for Humanoid Robot

< 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 ! ?

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

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]

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]

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],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

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

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

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]

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],[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

Face Identification for Humanoid Robot

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