Patent ID: 7236615

Claim:
A computer-implemented method of face detection and pose estimation, the method comprising the following steps: training a convolutional neural network to map facial images to points on a face manifold, parameterized by facial pose, and to map non-facial images to points away from the face manifold; and simultaneously determining, whether an image is a face from its proximity to the face manifold and an estimate of facial pose of that image from its projection to the face manifold; wherein the training step further comprises the step(s) of: optimizing a loss function of three variables, wherein said variables include image, pose, and face/non-face characteristics of an image; wherein the loss function is represented by: Loss ⁡ ( W ) = 1  S 1  ⁢ ∑ i ⁢ ⁢ εS 1 ⁢ L 1 ⁡ ( W , Z i , X i ) + 1  S 0  ⁢ ∑ i ⁢ ⁢ εS 1 ⁢ L 0 ⁡ ( W , X i ) ; where S 1 is the set of training faces, S 0 is the set of non-faces, L 3 (W,Z 1 ,X 1 ) and L 0 (W,X 1 ) are loss functions for a face sample (with a known pose) and non-face sample, respectively.