1. Cheng-Yi, Chuang (莊成毅), b99
Media IC & System Lab
Face Recognition: A Distributed Approach IoT / Video Sensor/ CV / Distributed System
Cheng-Yi, Chuang (莊成毅), b99

2. Outline
About the topic
Face recognition
Future Object

3. CV/Networking/Comm./Control
Media IC & System Lab
Image/Video/IC
IoT
Sensor networking
Camera (video sensor)
CV/Networking/Comm./Control CV
Anomaly Detection/Face Recognition
Big data
Limited bandwidth
Limited computation
Limited power
Face Recognition
A distributed computational approach

4. two meanings of “distributed”
Centralized vs. distributed
Distributed computation

5. Distributed system framework
Gateway
Filtering
Filtering
Gateway
Filtering
Filtering
Gateway
Filtering
Filtering

6. Face Recognition Block Diagram
How to cut? ; Gender, age, people…..
Recognize what?

7. Principle Component Analysis (PCA) (1/3)
For what? - Dimensionality reduction - Compute Eigenface (Sirovich and Kirby, 1987)
How? - Minimize the square projection error
Procedure (n-D -> k-D) - Preprocessing before doing PCA (feature scaling & mean normalization) - Compute covariance matrix Compute eigenvectors of matrix Σ
Face dataset
Eigenface
Σ= 1 𝑚 𝑋 𝑇 𝑋
PCA
[U, S, V] = svd(Sigma);

8. PCA (2/3) How to choose k? Projection onto Eigenfaces
Reconstruction / Classification / Recognition / (Whatever…)
𝑖=1 𝑘 𝑆 𝑖𝑖 𝑖=1 𝑚 𝑆𝑖𝑖 ≥99% 99% 𝑜𝑓 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑟𝑒𝑡𝑎𝑖𝑛𝑒𝑑
Ureduce = U(:, 1:k); Z = Ureduce’ * X;
X = Ureduce * Z

9. PCA (3/3) 32x32 in grayscale = 1024 dimensions
100 principle components
= 100 dimensions (display first 36)
PCA
Original faces
Principle components
Recovered faces
Stanford ML class by Andrew Ng

10. Related Techniques Face Detection Face Recognition Haar Features . . .
Principle Component Analysis (PCA) Linear Discriminant Analysis (LDA) Local Binary Pattern (LBP) Gabor features . . .

11. Future Object Get familiar with OpenCV/MATLAB
Study related techniques & papers
Implement the algorithm
Modify it into distributed manner
Hardware?
Of course, keep thinking, thinking, and thinking……

12. Thanks you!