This paper presents a novel method for reconstructing 3D geometry of camera motion and human-face model from a video sequence. The approach combines the concepts of Powell's line minimization with gradient descent. We adapted the line minimization with bracketing used in Powell's minimization to our method. However, instead of bracketing and searching deep down a direction for the minimum point along that direction as done in their line minimization, we achieve minimization by bracketing and searching for the direction in the bracket which provides a lower energy than the previous iteration. Thus, we do not need a large memory as required by Powell's algorithm. The approach to moving in a better direction is similar to classical gradient descent except that the derivative calculation and a good starting point are not needed. The system's constraints are also used to control the bracketing direction. The reconstructed solution is further improved using the Levenberg Marquardt algorithm. No average face model or known-coordinate markers are needed. Feature points defining the human face are tracked using the active appearance model. Occluded points, even in the case of self occlusion, do not pose a problem. The reconstructed space is normalized where the origin can be arbitrarily placed. To use the obtained reconstruction, one can rescale the computed volume to a known scale and transform the coordinate system to any other desired coordinates. This is relatively easy since the 3D geometry of the facial points and the camera parameters of all frames are explicitly computed. Robustness to noise and lens distortion, and 3D accuracy are also demonstrated. All experiments were conducted with an off-the-shelf digital camera carried by a person walking without using any dolly to demonstrate the robustness and practicality of the method. Our method does not require a large memory or the use of any particular, expensive equipment.

We present the use of a Modified Generalized Hough Transform (MGHT) and deformable contours for image data retrieval where a given contour, gray-scale, or color template image can be detected in the target image, irrespective of its position, size, rotation, and smooth deformation transformations. Potential template positions are found in the target image using our novel modified Generalized Hough Transform method that takes measurements from the template features by extending a line from each edge contour point in its gradient direction to the other end of the object. The gradient difference is used to create a relationship with the orientation and length of this line segment. Potential matching positions in the target image are then searched by also extending a line from each target edge point to another end along the normal, then looking up the measurements data from the template image. Positions with high votes become candidate positions. Each candidate position is used to find a match by allowing the template to undergo a contour transformation. The deformed template contour is matched with the target by measuring the similarity in contour tangent direction and the smoothness of the matching vector. The deformation parameters are then updated via a Bayesian algorithm to find the best match. To avoid getting stuck in a local minimum solution, a novel coarse-and-fine model for contour matching is included. Results are presented for real images of several kinds including bin picking and fingerprint identification.