Rotation Invariant Neural-Network

Based Face Detection

Rotation Invariant Neural-Network

Based Face Detection

OverviewOverview

Multiple Neural Networks Router Networks Detector Networks

Multiple Neural Networks Router Networks Detector Networks

Overview of how the algorithm works

Overview of how the algorithm works

Input and output of the router networkInput and output of the router network

Rotation Network:Outputs are generated as weighted

vectors

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

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

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

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 NetworkDetector 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

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

Adding False Positives to the training set as negative

images

Arbitration SchemeArbitration 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.

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:Empirical Results:

130 images, 511 faces 130 images, 511 faces

Sample Images

ConclusionsConclusions

Represents ways of integration multiple neural nets

Speed of implementation Face Detection VS Facial Recognition

Represents ways of integration multiple neural nets

Speed of implementation Face Detection VS Facial Recognition