Rotation Invariant Neural-Network Based Face Detection

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.

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

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