Methods and apparatuses for facilitating object recognition

1. A method comprising:

accessing data for a first object and data for a second object;

extracting a reference feature (P ji a ) from each of the first object and the second object, wherein the reference feature (P ji a ) is represented as:

P ji a =S a T ( Tr ji −m a )

where Tr ji is training data comprising a discriminative subspace and Tr ji ={Tr j1 , Tr j2 , . . . , Tr ji , . . . |j≦G} among G face classes,

where m a is a reference mean, and S a is a reference manifold and S a T is a transformation matrix of S a ;

comparing, by a processor, the first and second objects based at least in part upon the reference features from each of the first and second object, wherein the comparing comprises:

mapping the extracted first and second reference features on the discriminative subspace to obtain a first merged feature and a second merged feature;

determining a distance between the first and second merged features; and

determining whether the first object and the second object are the same object comprises determining whether the first object and the second object are the same object based at least in part upon the determined distance; and

determining, using a processor, whether the first object and the second object are the same object based at least in part upon the comparison.

2. The method of claim 1 , wherein determining whether the first object and the second object are the same object comprises determining that the first object and the second object are the same object when the determined distance is less than a predetermined threshold distance.

3. The method of claim 1 , further comprising generating the reference set from a set of reference objects.

4. The method of claim 1 , further comprising:

determining a discriminative subspace from the extracted reference features using a supervised learning technique, wherein the training results comprise the determined discriminative subspace.

5. The method of claim 1 , wherein the first object comprises a first face and the second object comprises a second face, and wherein determining whether the first object and the second object are the same object comprises determining whether the first face and the second face are sampled from the same person.

6. An apparatus comprising at least one processor and at least one memory storing computer program code, wherein the at least one memory and stored computer program code are configured to, with the at least one processor, cause the apparatus to at least:

access data for a first object and data for a second object;

extract a reference feature (P ji a ) from each of the first object and the second object, wherein the reference feature (P ji a ) is represented as:

P ji a =S a T ( Tr ji −m a )

where Tr ji is training data comprising a discriminative subspace and Tr ji ={Tr j1 , Tr j2 , . . . , Tr ji , . . . |j≦G} among G face classes,

where m a is a reference mean, and S a is a reference manifold and S a T is a transformation matrix of S a ;

compare the first and second objects based at least in part upon the reference features from each of the first and second object, wherein causing the apparatus to compare the first and second objects comprises causing the apparatus to:

map the extracted first and second reference features on the discriminative subspace to obtain a first merged feature and a second merged feature; and

determine a distance between the first and second merged features; and

the at least one memory and stored computer program code are configured to, with the at least one processor, cause the apparatus to determine whether the first object and the second object are the same object by determining whether the first object and the second object are the same object based at least in part upon the determined distance; and

determine whether the first object and the second object are the same object based at least in part upon the comparison.

7. The apparatus of claim 6 , wherein the at least one memory and stored computer program code are configured to, with the at least one processor, cause the apparatus to determine whether the first object and the second object are the same object by determining that the first object and the second object are the same object when the determined distance is less than a predetermined threshold distance.

8. The apparatus of claim 6 , wherein the at least one memory and stored computer program code are configured to, with the at least one processor, cause the apparatus to generate the reference set from a set of reference objects.

9. The apparatus of claim 6 , wherein the at least one memory and stored computer program code are configured to, with the at least one processor, further cause the apparatus to:

determine a discriminative subspace from the extracted reference features using a supervised learning technique, wherein the training results comprise the determined discriminative subspace.

10. The apparatus of claim 9 , wherein the supervised learning technique comprises linear discriminant analysis.

11. The apparatus of claim 6 , wherein the first object comprises a first face and the second object comprises a second face, and wherein the at least one memory and stored computer program code are configured to, with the at least one processor, cause the apparatus to determine whether the first object and the second object are the same object by determining whether the first face and the second face are sampled from the same person.

12. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program instructions stored therein, the computer-readable program instructions comprising program instructions configured to:

access data for a first object and data for a second object;

extract a reference feature (P ji a ) from each of the first object and the second object, wherein the reference feature (P ji a ) is represented as:

P ji a =S a T ( Tr ji −m a )

where Tr ji is training data comprising a discriminative subspace and Tr ji ={Tr j1 , Tr j2 , . . . , Tr ji , . . . |j≦G} among G face classes,

where m a is a reference mean, and S a is a reference manifold and S a T is a transformation matrix of S a ;

compare the first and second objects based at least in part upon the reference features from each of the first and second object, wherein the program code instructions configured to compare the first and second objects comprises program code instructions to:

map the extracted first and second reference features on the discriminative subspace to obtain a first merged feature and a second merged feature;

determine a distance between the first and second merged features; and

determine whether the first object and the second object are the same object comprises determining whether the first object and the second object are the same object based at least in part upon the determined distance; and

determine whether the first object and the second object are the same object based at least in part upon the comparison.

13. The method of claim 1 , wherein the object reference mean is calculated by the formula

m

i

=

∑

g

=

1

t

i

⁢

R

i

g

t

i

(

1

)

where R i is a reference object, R i ={R i 1 , R i 2 , . . . , R i t i }, i≦W, where W is the number of reference objects and t i is an image number in R i .

14. The method of claim 13 , where the data in R i is centralized at the mean to obtain normalized set R i ={ R i 1 , R i 2 , . . . R i t i }, where R i g =R i g −m i .

15. The method of claim 14 , wherein the manifold S a is calculated by a single value decomposition on the covariance of the matrix of R i .

16. The apparatus of claim 6 , wherein the object reference mean is calculated by the formula

m

i

=

∑

g

=

1

t

i

⁢

R

i

g

t

i

(

1

)

where R i is a reference object, R i ={R i 1 , R i 2 , . . . R i t i }, i≦W, where W is the number of reference objects and t i is an image number in R i .

17. The apparatus of claim 16 , where the data in R i is centralized at the mean to obtain normalized set R i ={ R i 1 , R i 2 , . . . R i t i }, where R i g =R i g −m i .

18. The apparatus of claim 17 , wherein the manifold S a is calculated by a single value decomposition on the covariance of the matrix of R i .