Rotation Invariant Neural-Network Based Face Detection

1. Rotation Invariant Neural-Network Based Face Detection

2. Overview • Multiple Neural Networks • Router Networks • Detector Networks

3. Overview of how the algorithm works

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