초록 ▼

In an apparatus and method for generating a three-dimensional representation from a two-dimensional image, a memory device stores information for processing a two-dimensional image and for generating a three-dimensional image from the two-dimensional image, a processing device processes a digital re

In an apparatus and method for generating a three-dimensional representation from a two-dimensional image, a memory device stores information for processing a two-dimensional image and for generating a three-dimensional image from the two-dimensional image, a processing device processes a digital representation of an image by generating a two-dimensional image from the digital representation and by generating a three-dimensional image corresponding to the two-dimensional image, and an output device outputs a three-dimensional image and a digital signal representation of the three-dimensional image.

대표청구항 ▼

What is claimed is: 1. A method in a system for generating a three-dimensional face representation having at least one processor coupled to a memory, comprising: finding a face image in a two-dimensional image obtained from an image capture device using the processor and a two-dimensional face mode

What is claimed is: 1. A method in a system for generating a three-dimensional face representation having at least one processor coupled to a memory, comprising: finding a face image in a two-dimensional image obtained from an image capture device using the processor and a two-dimensional face model built from a two-dimensional training set stored in the memory, wherein finding the face image comprises iteratively estimating a face image location based on an initial estimate of the face image location and a new estimate of a location of one of a plurality of model feature annotations; and using a three-dimensional face model built from a three-dimensional training set stored in the memory to generate the three-dimensional face representation from the face image. 2. The method of claim 1, comprising building the two-dimensional face model from the two-dimensional training set. 3. The method of claim 1, wherein using a three-dimensional face model comprises using a three-dimensional face model built from three-dimensional training sets each comprising sets of three modeled coordinate variables. 4. The method of claim 1, wherein finding the face image comprises: annotating the two-dimensional face model with a plurality of model feature annotations; using a mean observation vector to estimate a face image location; using a feature search specific to one of the plurality of model feature annotations to estimate a first feature location; and using at least the face image location and the first feature location to find the face image. 5. The method of claim 4, wherein finding the face image comprises: using a feature search specific to another of the plurality of model feature annotations to estimate a second feature location; and using at least the face image location, the first feature location, and the second feature location to find the face image. 6. The method of claim 1, wherein building the two-dimensional face model from the two-dimensional training set comprises: constructing observation vectors from a plurality of manually annotated feature annotations; normalizing the observation vectors; constructing a covariance matrix from the normalized observation vectors; and performing an eigen analysis on the covariance matrix to build the two-dimensional face model. 7. The method of claim 1, comprising building the three-dimensional face model from three-dimensional training sets each comprising sets of three modeled coordinate variables. 8. The method of claim 1, further comprising processing a financial transaction in conjunction with generating a three-dimensional face representation by inputting the three-dimensional face representation into an image processing routine, and executing the image processing routine to facilitate the transaction. 9. The method of claim 1, comprising: receiving the two-dimensional image at a server from a remote computer; and transmitting the three-dimensional face representation from the server to the remote computer. 10. A method in a system for generating a three-dimensional face representation having at least one processor coupled to a memory, comprising: building a two-dimensional face model from a two-dimensional training set stored in the memory; automatically finding a face image in a two-dimensional image obtained from an image capture device using the processor and the two-dimensional face model, wherein finding the face image comprises iteratively estimating a face image location based on an initial estimate of the face image location and a new estimate of a location of one of a plurality of model feature annotations; and using a three-dimensional face model built from a three-dimensional training set stored in the memory to generate the three-dimensional face representation from the face image. 11. The method of claim 10, wherein using a three-dimensional face model comprises using a three-dimensional face model built from three-dimensional training sets each comprising sets of three modeled coordinate variables. 12. An apparatus for generating a three-dimensional face representation, comprising: a memory that at least temporarily stores a two-dimensional face model built from a two-dimensional training set and a three dimensional face model built from a three-dimensional training set; at least one processor that automatically finds a face image in a two-dimensional image using the two-dimensional face model and generates the three-dimensional face representation from the face image using the three-dimensional face model, wherein finding the face image comprises iteratively estimating a face image location based on an initial estimate of the face image location and a new estimate of a location of one of a plurality of model feature annotations. 13. The apparatus of claim 12, wherein the at least one processor: constructs observation vectors from a plurality of manually annotated feature annotations; normalizes the observation vectors; constructs a covariance matrix from the normalized observation vectors; and builds the two-dimensional face model by performing an eigen analysis on the covariance matrix. 14. The apparatus of claim 12, wherein the three-dimensional face model is built from three-dimensional training sets and each three-dimensional training set comprises sets of three modeled coordinate variables. 15. The apparatus of claim 12, further comprising an additional processor and wherein at least one of the additional processor and the at least one processor builds the three-dimensional face model from three-dimensional training sets and each three-dimensional training set comprises sets of three modeled coordinate variables. 16. The apparatus of claim 12, wherein said processor processes a financial transaction. 17. The apparatus of claim 12, wherein the at least one processor: annotates the two-dimensional face model with a plurality of model feature annotations; estimates a face image location by using at least one mean observation vector; estimates a first feature location by using a feature search specific to one of the plurality of model feature annotations; and finds the face image by using at least the face image location and the first feature location. 18. The apparatus of claim 17, wherein the at least one processor: estimates a second feature location by using a feature search specific to another of the plurality of model feature annotations; and finds the face image by using at least the face image location, the first location, and the second feature location.