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 PhươngHV: Võ Tấn Tài

Đại Học Quốc Gia Thành Phố Hồ Chí MinhTrường Đại Học Khoa Học Tự Nhiên

Khoa Điện Tử - Viễn ThôngCao 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 KêCao học khoá 23| Đại học Khoa Học Tự Nhiên

Agenda

1. EIGENFACES overview2. PCA3. Results4. Recent Advances in Face Recognition5. References


Image Face Detection

Face Feature Extraction

Feature Matching Decision Maker

Output




Output

Ana Bob John Puppy LiLy





Ouput





OutputLiLy


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 Recognition1. 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.

http://www.face-rec.org/journals-books/Delac_Grgic_Bartlett_Recent_Advances_in_Face_Recognition.pdf


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.

ung dung pca vao nhan dang khuong mat nguoi

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