System and method for rapid face recognition

Patent metadata

Field	Value
Publication number	US-9275309-B2
Application number	US-201414449352-A
Country	US
Kind code	B2
Filing date	Aug 1, 2014
Priority date	Aug 1, 2014
Publication date	Mar 1, 2016
Grant date	Mar 1, 2016

Abstract

A face recognition method is provided to use sparse representation and regularized least squares-based classification on a computing device. The method includes obtaining an image to be recognized as a test sample y and a set of training images of certain subjects as training sample matrix T, obtaining a sparse representation of the test sample and the training samples including an initial estimation of a sparse vector a, and constructing a new face dictionary comprising training samples with non-zero corresponding coefficients in the sparse vector a for the initial estimation. The method also includes obtaining new coefficients by solving a regularized least squares problem based on the constructed new face dictionary, and determining a face identity of the test sample based on minimum class residual calculated by using the new coefficients.

First claim

What is claimed is:

1. A face recognition method using sparse representation and regularized least squares-based classification on a computing device, the method comprising:
obtaining an image to be recognized as a test sample y and a set of training images of certain subjects as training sample matrix T;
obtaining a sparse representation of the test sample and the training samples including an initial estimation of a sparse vector a;
constructing a new face dictionary comprising training samples with non-zero corresponding coefficients in the sparse vector a for the initial estimation;
obtaining new coefficients by solving a regularized least squares problem based on the constructed new face dictionary; and
determining a face identity of the test sample based on minimum class residual calculated by using the new coefficients.

2. The face recognition method according to claim 1 , further including:
presenting the face identity of the test sample to a user of the computing device.

3. The face recognition method according to claim 1 , further including:
determining whether to use a standard sparse coding optimization problem or to use an approximated sparse coding optimization problem to obtain the initial estimation of the sparse vector a,
wherein the standard sparse coding optimization problem uses an l 1 minimization algorithm and the approximated sparse coding optimization problem requires that a least squares problem is first solved and a threshold is used to suppress most values to zero.

4. The face recognition method according to claim 3 , wherein:
the test sample y is represented as a sparse linear combination of samples in T as:
y=Ta+e, wherein eε d is dense noise and aε n is the sparse vector with nonzero elements corresponding to few samples in T.

5. The face recognition method according to claim 4 , wherein:
when the standard sparse coding optimization problem is used, the coefficients of the sparse vector a is estimated by solving the sparse coding optimization problem by

a
=

arg
⁢

⁢

min
a

⁢



y
-
Ta



2
2

+

λ
⁢


a


1

.

6. The face recognition method according to claim 4 , wherein:
when the approximated sparse coding optimization problem is used, the coefficients of the sparse vector a is estimated by solving the approximated sparse coding optimization problem by

a
=

arg
⁢

⁢

min
a

⁢



y
-
Ta



2
2

+

λ
⁢


a


2
2

.

7. The face recognition method according to claim 1 , wherein constructing the new face dictionary further includes:
provided the function ƒ(a i ), where a i is the segment of a associated with class i, be given as

f
⁡

(

a
i

)

=

(

0
,

if
⁢

⁢

a
i

=
0

1
,

otherwise

,

constructing the new dictionary T as T=[ƒ(a i )×T i , . . . , ƒ(a c )×T c ]ε d×n , wherein × denotes a convolution operator.

8. The face recognition method according to claim 7 , wherein obtaining the new coefficients further includes:
obtaining new estimation vector can be obtained by solving the regularized least squares (RLS) problem

f
=

arg
⁢

⁢

min
f

⁢



y
-
Tf



2
2

+

λ
⁢



Assignees

• Tcl Res America Inc

Inventors

• Iliadis Michael
• Wang Haohong

Classifications

• G06V10/772

  Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries · CPC title

• G06V40/172 Primary

  Classification, e.g. identification · CPC title

• G06F18/28

  Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries · CPC title

• G06V10/513

  Sparse representations · CPC title

• G06K9/66 Primary

  Physics · mapped topic