Ung Dung PCA vao nhan dang khuong mat nguoi

Báo Cáo Seminar Môn Học: Phương Pháp Phân Tích Thống Kê
Seminar: Ứng Dụng Phương Pháp Phân Tích Thành
Phần Chính Vào Nhận Dạng Khuông Mặt Người
GVPT: PGS.TS Nguyễn Hữu Phương
HV: Võ Tấn Tài
Đại Học Quốc Gia Thành Phố Hồ Chí Minh
Trường Đại Học Khoa Học Tự Nhiên
Khoa Điện Tử - Viễn Thông
Cao Học Khoá 23 Chuyên Ngành Điện Tử, Viễn Thông Và Máy Tính
Tp. Hồ Chí Minh, Tháng 6 năm 2014

Phương Pháp Phân Tích Thống Kê
Cao học khoá 23| Đại học Khoa Học Tự Nhiên
Agenda
1. EIGENFACES overview
2. PCA
3. Results
4. Recent Advances in Face Recognition
5. References

Image Face Detection
Face Feature
Extraction
Feature Matching Decision Maker
Output

Face Feature
Extraction
Output
Ana Bob John Puppy LiLy

Face Feature
Extraction
Ouput

Face Feature
Extraction
Output
LiLy

Problems
Each image is a n x m matrix of pixels. Convert it into a nm vector by stacking the
columns.
A small image is 100x100 -> a 10000 element vector, i.e. a point in a 10000 dimension
space!

PCA
To perform PCA several steps are undertaken:
Stage 1: Subtract the Mean of the data from each variable.
Stage 2: Calculate and from a covariance Matrix.
Stage 3: Calculate Eigenvectors and Eigenvalue form the
covariance Matrix.
Stage 4: Chose a Feature Vector ( a fancy name for a matrix of
vectors).
Stage 5: Multiply the transposed Feature Vectors by the
transpose adjusted data.

STAGE 1: Mean Subtraction
M = compute average vector
Subtract M from each vector  Zero centered distribution

STAGE 2: Covariance Matrix
compute covariance matrix
C =

STAGE 3: Eigenvectors and Eigen values

STAGE 4: Feature Vectors

STAGE 5: Transposition
The final stage in PCA is to take the transpose of the feature vector matrix and
multiply it on the left of the transposed adjusted data set (the adjusted data set is
from Stage 1 where the mean was subtracted from the data).
The Eigen Object Recognizer class performs all of this and then feeds the
transposed data as a training set into a Neural Network. When it is passed an
image to recognize it performs PCA and compares the generated Eigen values and
Eigenvectors to the ones from the training set the Neural Network then produces
a match if one has been found or a negative match if no match is found. The is a
little more to it than this however the use of Neural Networks is a complex subject
to cover and is not the object of this article.

Results

Recent Advances In Face Recognition
A Google Scholar search for papers with the words “face recognition” yielded 1,100 papers
in 2000 and 9,190 papers in 2012.
Link: Recent Advances In Face Recognition
1. Image Compression in Face Recognition - a Literature Survey.
2. New Parallel Models for Face Recognition.
3. Robust Face Recognition System Based on a Multi-Views Face Database.
4. Face Recognition by Discriminative Orthogonal Rank-one Tensor Decomposition.
5. Intelligent Local Face Recognition.
6. Generating Optimal Face Image in Face Recognition System.
7. Multi-resolution Methods in Face Recognition.
8. Illumination Normalization using Quotient Image-based Techniques.
9. Liveness Detection for Face Recognition.
10. 2D-3D Mixed Face Recognition Schemes.
11. Recognizing Face Images with Disguise Variations.
12. Discriminant Subspace Analysis for Uncertain Situation.
13. Blood Perfusion Models for Infrared Face Recognition.
14. Discriminating Color Faces For Recognition.
15. A Novel Approach to Using Color Information in Improving Face Recognition Systems
Based on Multi-Layer Neural Networks.

References
[1] An Efficient Method For Face Recognition Using Principal
Component Analysis (PCA), Dr. Tamilnadu, B. Dr. V.Cyril Raj.
[2] Performance Comparison of Principal Component Analysis-Based Face
Recognition in Color Space, Seunghwan Yoo, Dong-Gyu Sim, Young-Gon Kim
and Rae-Hong Park.
[3] Face Recognition: Where We Are and Where To Go From Here, Michael Jones.
[4] Recent Advances in Face Recognition, Kresimir Delac, Mislav Grgic
and Marian Stewart Bartlett.
[5] Face Recognition using Principle Component Analysis, Kyungnam Kim.
[6] Recognizing faces with PCA and ICA, Bruce A. Draper,Kyungim Baek,
Marian Stewart Bartlett and J. Ross Beveridge.

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