Rotation Invariant
Neural-Network Based
Face Detection
Overview
 Multiple Neural Networks
 Router Networks
 Detector Networks
 Overview of how the algorithm works
Input and output of the
router network
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
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
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
Adding False Positives to the training set
as negative images
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.
Empirical Results:
 130 images, 511 faces
Sample Images
Conclusions
 Represents ways of integration
multiple neural nets
 Speed of implementation
 Face Detection VS Facial Recognition