Face annotation for photo management

1. A method implemented by a computing device, the method comprising:

deriving a similarity measure by integrating results from content-based image retrieval and face recognition into a Bayesian framework to obtain a maximum posterior estimation that extended face area contextual features, face appearance, and face components associated with a set of facial features match a particular set of features identified in a probability model, the probability model mapping one or more sets of sample facial features independent of any specific individual to corresponding names of individuals; and

inferring a name of a particular person that corresponds to the facial features as a function of the similarity measure.

2. The method as of claim 1 , wherein modeling the similarity measure further comprises marginalizing the similarity measure over one or more missing features to arrive at a similarity measure for the one or more missing features.

3. The method of claim 1 , wherein deriving the similarity measure further comprises comparing extended face area contextual features from a first digital image with contextual features extracted from a second digital images only if the first and second digital images were generated within a configurable number of days from one another, the second digital image being from a training data set.

4. The method of claim 1 , wherein inferring the name further comprises generating a candidate name list by integrating priors of both intra-personal variations Ω I and inter-personal variations Ω E into the similarity measure.

5. The method of claim 1 :

wherein inferring the name further comprises searching historical labeling results to generate a candidate name list; and

wherein the method further comprises automatically annotating the face with the name if the similarity measure meets or exceeds a confidence threshold.

6. The method of claim 1 , wherein the face is an unknown face, and wherein inferring the name further comprises:

integrating priors of both intra-personal variations Ω I and inter-personal variations Ω E into the similarity measure to generate a candidate name list, the candidate name list comprising the name;

based on a nearest neighbor or K-nearest neighbor algorithm, among K nearest labeled faces to the unknown face, sorting names in the candidate name list according to a sum of similarities measures corresponding to the unknown face; and

presenting the candidate list to a user for annotation.

7. The method of claim 1 , wherein inferring the name further comprises:

obtaining labeling result from a user;

storing the labeling result into a historical record;

performing multi-class classifications to identify a candidate name list as a function of the historical record; and

wherein annotating the face further comprises presenting the candidate name list to a user, the name being in the candidate name list.

8. A computer-readable medium comprising computer-program instructions executable by a processor for performing the method of claim 1 .

9. A computer-readable medium comprising computer-program instructions executable by a processor, the computer-program instructions comprising instructions for:

training a probability model to map one or more sets of sample facial features to corresponding names of individuals;

detecting a face in a digital image;

extracting facial features from the face;

modeling a similarity measure as a posterior probability that the facial features match a particular set of features identified in a probability model, the similarity measure being statistically learned;

inferring a name as a function of the similarity measure; and

annotating the face with the name.

10. The computer-readable medium of claim 9 , wherein the digital image is from a family photo album.

11. The computer-readable medium of claim 9 , wherein the facial features comprise face appearance, face component, and extended face area contextual features.

12. The computer-readable medium of claim 9 , wherein the facial features comprise face appearance, face component, and extended face area contextual features, the face appearance and components being modeled in eigenspaces.

13. The computer-readable medium of claim 9 , wherein the instructions for modeling the similarity measure further comprise computer-program instructions for integrating results from content-based image retrieval and face recognition algorithms into a Bayesian framework.

14. The computer-readable medium of claim 9 , wherein the instructions for modeling the similarity measure further comprise computer-program instructions for marginalizing the similarity measure over one or more missing features to arrive at a similarity measure for the one or more missing features.

15. The computer-readable medium of claim 9 , wherein the facial features comprise contextual features, and wherein the instructions for modeling the similarity measure further comprise computer-program instructions for comparing contextual features of the face with contextual features from other digital images only if the other digital images were generated within a configurable number of days from when the digital image was created.

16. The computer-readable medium of claim 9 , wherein the instructions for inferring the name further comprise computer-program instructions for searching historical labeling results to generate a candidate name list.

17. The computer-readable medium of claim 9 , wherein the instructions for inferring the name further comprise computer-program instructions for generating a candidate name list by integrating priors of both intra-personal variations Ω I and inter-personal variations Ω E into the similarity measure.

18. The computer-readable medium of claim 9 , wherein the face is an unknown face, and wherein the instructions for inferring the name further comprise computer-program instructions for:

integrating priors of both intra-personal variations Ω I and inter-personal variations Ω E into the similarity measure to generate a candidate name list, the candidate name list comprising the name;

based on a nearest neighbor or K-nearest neighbor algorithm, among K nearest labeled faces to the unknown face, sorting names in the candidate name list according to a sum of similarities measures corresponding to the unknown face; and

presenting the candidate list to a user for annotation.

19. The computer-readable medium of claim 9 , wherein the instructions for inferring the name further comprise computer-program instructions for:

obtaining labeling result from a user;

storing the labeling result into a historical record;

performing multi-class classifications to identify a candidate name list as a function of the historical record; and

wherein annotating the face further comprises presenting the candidate name list to a user, the name being in the candidate name list.

20. The computer-readable medium of claim 9 , further comprising computer-program instructions for presenting the name in a user interface (UI), the UI allowing a user to accept or reject the name.

21. A computing device comprising a processor and a memory, the memory comprising computer-program instructions executable by the processor, the computer-program instructions comprising instructions for:

extracting facial features from a detected face in digital image data;

deriving a similarity measure by integrating results from content-based image retrieval and face recognition into a Bayesian framework to obtain a maximum posterior estimation that extended face area contextual features, face appearance, and face components associated with the facial features match a particular set of features identified in a probability model, the probability model mapping one or more sets of sample facial features independent of any specific individual to corresponding names of individuals; and

inferring a name as a function of the similarity measure.

22. The computing device of claim 21 , wherein the instructions for modeling the similarity measure further comprise computer-program instructions for marginalizing the similarity measure over one or more missing features to arrive at a similarity measure for the one or more missing features.

23. The computing device of claim 21 , wherein the digital image is a first digital image, and wherein the instructions for deriving the similarity measure further comprise computer-program instructions for comparing the extended face area contextual features with contextual features extracted from a second digital images only if the first and second digital images were generated within a configurable number of days from one another, the second digital image being from a training data set.

24. The computing device of claim 21 , wherein the instructions for inferring the name further comprise computer-program instructions for generating a candidate name list by integrating priors of both intra-personal variations Ω I and inter-personal variations Ω E into the similarity measure.

25. The computing device of claim 21 :

wherein the instructions for inferring the name further comprise computer-program instructions for searching historical labeling results to generate a candidate name list; and

wherein the computer program instructions further comprise automatically annotating the face with the name if the similarity measure meets or exceeds a confidence threshold.

26. The computing device of claim 21 , wherein the face is an unknown face, and wherein the instructions for inferring the name further comprise computer-program instructions for:

integrating priors of both intra-personal variations Ω I and inter-personal variations Ω E into the similarity measure to generate a candidate name list, the candidate name list comprising the name;

based on a nearest neighbor or K-nearest neighbor algorithm, among K nearest labeled faces to the unknown face, sorting names in the candidate name list according to a sum of similarities measures corresponding to the unknown face; and

presenting the candidate list to a user for annotation.

27. The computing device of claim 21 , wherein the instructions for inferring the name further comprise computer-program instructions for:

obtaining labeling result from a user;

storing the labeling result into a historical record;

performing multi-class classifications to identify a candidate name list as a function of the historical record; and

wherein annotating the face further comprises presenting the candidate name list to a user, the name being in the candidate name list.

28. The computing device of claim 21 , wherein the computer-program instructions further comprise instructions for annotating the face with the name.

29. A computing device comprising:

means for deriving a similarity measure by integrating results from content-based image retrieval and face recognition into a Bayesian framework to obtain a maximum posterior estimation that extended face area contextual features, face appearance, and face components associated with a set of facial features match a particular set of features identified in a probability model, the probability model mapping one or more sets of sample facial features independent of any specific individual to corresponding names of individuals; and

means for inferring a name of a particular person that corresponds to the facial features as a function of the similarity measure.

30. The computing device of claim 29 , wherein the facial features are associated with a face extracted from a digital photograph.

31. The computing device of claim 29 , wherein the means for modeling the similarity measure further comprise means for marginalizing the similarity measure over one or more missing features to arrive at a similarity measure for the one or more missing features.

32. The computing device of claim 29 :

wherein the digital image is a first digital image; and

wherein the means for deriving the similarity measure further comprise means for comparing the extended face area contextual features with contextual features extracted from a second digital images only if the first and second digital images were generated within a configurable number of days from one another, the second digital image being from a training data set.

33. The computing device of claim 29 , wherein the means for inferring the name further comprise means for generating a candidate name list by integrating priors of both intra-personal variations Ω I and inter-personal variations Ω E into the similarity measure.

34. The computing device of claim 29 :

wherein the means for inferring the name further comprise means for searching historical labeling results to generate a candidate name list; and

wherein the computing device further comprise means for automatically annotating the face with the name if the similarity measure meets or exceeds a confidence threshold.

35. The computing device of claim 29 , wherein the face is an unknown face, and wherein the means for inferring the name further comprise:

means for integrating priors of both intra-personal variations Ω I and inter-personal variations Ω E into the similarity measure to generate a candidate name list, the candidate name list comprising the name;

means for based on a nearest neighbor or K-nearest neighbor algorithm, among K nearest labeled faces to the unknown face, sorting names in the candidate name list according to a sum of similarities measures corresponding to the unknown face; and

means for presenting the candidate list to a user for annotation.

36. The computing device of claim 29 , wherein the means for inferring the name further comprises:

means for obtaining labeling result from a user;

means for storing the labeling result into a historical record;

means for performing multi-class classifications to identify a candidate name list as a function of the historical record; and

means for wherein annotating the face further comprises presenting the candidate name list to a user, the name being in the candidate name list.