1. Rotation Invariant Neural-Network Based Face Detection

2. Overview Multiple Neural Networks Router Networks Detector Networks
Makes template based face detector rotation invariant
Their system directly analyzes image intensities using neural networks, whose parameters are learned automatically from training examples.
Router classifies if the image contains a face and the angle of rotation.

3. Overview of how the algorithm works
Image is scaled into a pyramid
Processed in 20 by 20 squares and passed through the router network
Derotated image passed along to the facial detection algorithm
Image is preprocessed facial detection

4. Input and output of the router network

5. Rotation Network: Outputs are generated as weighted vectors
Average of the weighted vectors is interpreted as an angle
1048 training images labeled by face, eyes, tip of the nose, corners and centers of the mouth
Each training face is rotated 15 times in a circle

6. Rotation Neural Net Description
400 layers on the input layer (20X20)
Hidden layer of 15 units, output layer of 36 units.
Hyperbolic tangent activation function
Standard error back propigation

7. Detector Network Identical to the routing network.
Trained by positive (contains faces) and negative images (does not contain faces).
Weights are initially random for the first training iteration.
Training on non-face images, add false positives to the non-image

8. Adding False Positives to the training set as negative images

9. Arbitration Scheme
Detection of Different Faces at different angles in the same image
Detections are placed in 4 dimensional space - x,y,angle, pyramid level, quantized in 10 degree increments.
Two independently trained networks are ANDed to improve the success rate.

11. Empirical Results:
130 images, 511 faces

15. Sample Images

17. Conclusions Represents ways of integration multiple neural nets
Speed of implementation
Face Detection VS Facial Recognition