Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTEC
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The document outlines the standard procedures and techniques used in face recognition and deep learning, including image capturing, face detection, alignment, and matching. It discusses various methods such as PCA, eigenfaces, and other advanced techniques to enhance recognition accuracy while addressing limitations like occlusion, lighting conditions, and pose variations. Deep learning developments in neural networks and applications like CNNs are also covered, emphasizing their importance in modern face recognition systems.
Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTEC
- 2. Standard procedure • Image capturing: camera, webcam, surveillance • Face detection: locate faces in the image • Face alignment: normalize size, rectify rotation • Face matching • 1:1 Face verification • 1:N Face recognition
- 3. Viola-Jones Haar-like detector (OpenCV haarcascade_frontalface_alt2.xml) face size~35x35 to 80x80 pixels too small occlusion rotation Recognition = compare these faces to known faces
- 4. Controlled environment face size 218x218 pixels Viola-Jones eye detector Eyes distance = 81 pixels Eyes angle = -0.7 degrees Face size = 180x200 pixels Eyes distance = 100 pixels Eyes angle = 0 degrees
- 6. Comparing face • Face image • Bitmap of size 180x200 pixels • Grayscale (0-255) • 36,000 values/face image • Given 2 face images x1 and x2 • x1(x,y) - x2(x,y) • | x1(x,y) - x2(x,y) | • (x1(x,y) - x2(x,y)) 2 • What should be used?
- 7. Basic Maths • 1 Face image = 1 vector • 36,000 dimensions (d) • matrix with 1 column • Distance • Euclidean distance • Norm-p distance • Norm-1 distance • Norm-infinity distance
- 8. Pixels importance and projection • Not all pixels have the same importance • Pixel with low variation -> not important • Pixel with large variation -> could be important Projection When ||w||=1, wTx is the projection of x on axis w w
- 9. Subspace projection • What should be the axis w? • How many axis do we need?
- 10. Principal Component Analysis PCA (1) • Basic idea • Measure of information = variance • Variance of z1,…,zN for real numbers zt • Given a set of face vectors x1,…,xN and axis w Variance of w T x1,…,w T xN is Covariance matrix
- 11. Principal Component Analysis PCA (2) • Best axis w is obtained by maximizing w T Cw with constraint ||w||=1 • w is an eigenvector of C : Cw = a w • Variance w T Cw=a is the corresponding eigenvalue of w • PCA • Construct Covariance matrix C • Eigen-decompose C • Select m largest eigenvectors
- 12. Eigenface (1) • What is the problem with face data? • Solution Dot matrix dxd matrix NxN matrix
- 13. Eigenface (2) • We work with vectors of projected values x1 x2 … x40 x Enrollment Template
- 14. Eigenface (3) • Vector of raw intensity: 36,000 dimensions • Vector of Eigenface coefficients: 10 dimensions • Large Eigenface = large variation • Small Eigenface = noise
- 15. Related techniques • Fisherface (LDA) • Nullspace LDA • Laplacianface • Locality Sensitive Discriminant Analysis • 2DPCA • 2DLDA • 2DPCA+2DLDA
- 16. Result on ORL (~10 years ago) Techniques Accuracy #dim Eigenface 90-95 200 Fisherface 91-97 50 NLDA 92-97 40 Laplacianface 89-95 50 LSDA 91-97 50 2DPCA 91.5 2DLDA 90.5 2DPCA+2DLDA 93.5
- 17. Limitations • Occlusion: glasses, beard • Lighting condition • Facial expression • Pose • Make-up
- 18. Evaluation • Accuracy: find closest template and check the ID • Verification (access control) • Live captured image VS. stored image • We have distance -> Should we accept or not? • False Accept (FA) VS. False Reject (FR) • From a set of face images • Compute distances between all pair • Select threshold T that gives 0 FA and X FR • Number of tries distance T
- 19. Labeled Faces in the Wild • Large number of subjects (>5,000) • Unconstrained conditions • Human performance 97-99% • Traditional methods fail • New alignment technique: funneling
- 20. LFW results Use outside data to train the model
- 21. Deep Learning
- 22. Neural Network timeline McCulloch & Pitts Neuron model (1943) Perceptron limitation (1969) Backprop algorithm 70-80’s SVM (1992) Deep Learning (2006)
- 23. • Return of Neural Network • Focus on Deep Structure • Take advantage of today computing power
- 24. Neural Networks (1) • Neurons are connected via synapse • A neuron receives signals from other neurons • When the activation reaches a threshold, it fires a signal to other neurons http://en.wikipedia.org/wiki/Neuron
- 25. Neural Networks (2) • Universal Approximator • Classical structure: MLP • #hidden nodes, learning rate • Backprop algorithm • Gradient • Direction of change that increases value of objective function • Vector of partial derivatives wrt. each parameters • Work on all structures, all objective functions • Stoping criteria, local optima, gradient vanishing/exploding
- 26. Deep Learning • 2006 Hinton et al.: layer by layer construction -> pre-training • Stack of RBMs, Stack of Autoencoders • Convolutional NN (CNN) • Shared weights • Take advantage of GPU
- 27. CNN today • Common components • Convolution layer, Max-pooling layer • ReLU • Drop-out, Sampling+flip training data • GPU • Tools: Caffe, TensorFlow, Theano, Torch • Structure: LeNet, AlexNet, GoogLeNet
- 28. LeNet
- 29. LeNet AlexNet
- 31. LeNet AlexNet GoogLeNet Microsoft deep residual network: 150 layers!
- 32. DeepID (Sun et al. CVPR 2014) • 160 dim, 60 regions, flipped • 19,200 dimensions!! • Input to other model • CelebFace • Refine training