Sample-Relaxed Two-Dimensional Color Principal Component Analysis for Face Recognition and Image Reconstruction

03/10/2018 ∙ by Meixiang Zhao, et al. ∙ NetEase, Inc 0

A sample-relaxed two-dimensional color principal component analysis (SR-2DCPCA) approach is presented for face recognition and image reconstruction based on quaternion models. A relaxation vector is automatically generated according to the variances of training color face images with the same label. A sample-relaxed, low-dimensional covariance matrix is constructed based on all the training samples relaxed by a relaxation vector, and its eigenvectors corresponding to the r largest eigenvalues are defined as the optimal projection. The SR-2DCPCA aims to enlarge the global variance rather than to maximize the variance of the projected training samples. The numerical results based on real face data sets validate that SR-2DCPCA has a higher recognition rate than state-of-the-art methods and is efficient in image reconstruction.

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1 Introduction

In this paper, we present a sample-relaxed color two-dimensional principal component analysis (SR-2DCPCA) approach for face recognition and image reconstruction based on quaternion models. Different from 2DPCA [23] and 2DCPCA [8], SR-2DCPCA utilizes the variances of training samples with the same label, aims to maximize the variance of the whole (training and testing) projected images, and has comparable feasibility and effectiveness with state-of-the-art methods.

Color information is one of the most important characteristics in reflecting structural information of a face image. It can help human being to accurately identify, segment or watermark color images (see [1], [3]-[7], [13, 17, 20, 21, 24, 25] for example). Various traditional methods of (grey) face recognition has been improved by fully exploiting color cues. Torres et al. [18] applied the traditional PCA into R, G and B color channels, respectively, and got a fusion of the recognition results from three color channels. Xiang, Yang and Chen [22]

proposed a CPCA approach for color face recognition. They utilized a color image matrix-representation model based on the framework of PCA and applied 2DPCA to compute the optimal projection for feature extraction. Recently, the quaternion PCA (QPCA)

[2], the two-dimensional QPCA (2DQPCA) and bidirectional 2DQPCA [16], and kernel QPCA (KQPCA) and two-dimensional KQPCA [3], have been proposed to generalize the conventional PCA and 2DPCA to color images, using the quaternion representation. These approaches have achieved a significant success in promoting the robustness and the ratio of face recognition by utilizing color information.

The PCA-like methods for face recognition are based on linear dimension-reducing projection. The projection directions are exactly orthonormal eigenvectors of the covariance matrix of training samples, with the aim to maximize the total scatter of the projected training images. Sirovich and Kirby [14, 10] applied the PCA approach to the representation of (grey) face images, and asserted that any face image can be approximately reconstructed by a facial basis and a mean face image. Based on this assertion, Turk and Pentland [19] proposed a well-known eigenface method for face recognition. Following that, many properties of PCA have been studied and PCA has gradually become one of the most powerful approaches for face recognition [12]. As a breakthrough development, Yang et al. [23] proposed the 2DPCA approach, which constructs the covariance matrix by directly using 2D face image matrices. Generally, the covariance matrix in 2DPCA is of a smaller dimension than that in PCA, which reduces storage and the computational operations. Moreover, the 2DPCA approach is convenient to apply spacial information of face images, and achieves a higher face recognition rate than PCA in most cases.

With retaining the advantages of 2DPCA, 2DCPCA recently proposed in [8] is based on quaternion matrices rather than quaternion vectors, and hence can fully utilize color information and the spacial structure of face images. In this method, the generated covariance matrix is Hermitian and low-dimensional. The projection directions are eigenvectors of the covariance matrix corresponding to the largest eigenvalues. We find that 2DCPCA ignores label information (if provided) and scatter of training samples with the same label. If these information is considered when constructing a covariance matrix, the recognition rate of 2DCPCA will be improved. As far as our knowledge, there has been no approaches of face recognition based on the framework of 2DCPCA using these information.

The paper is organized as follows. In section 2, we recall two-dimensional color principle component analysis approach proposed in [8]. In section 3, we present a sample-relaxed two-dimensional color principal component analysis approach based on quaternion models. In section 4, we provide the theory of the new approach. In section 5, numerical experiments are conduct by applying the Georgia Tech face database and the color FERET face database. Finally, the conclusion is draw in section 6.

2 2dcpca

We firstly recall the two-dimensional color principle component analysis (2DCPCA) from [8].

Suppose that an quaternion matrix is in the form of , where are three imaginary values satisfying

(2.1)

and , . A pure quaternion matrix is a matrix whose elements are pure quaternions or zero. In the RGB color space, a pixel can be represented with a pure quaternion, , where stand for the values of Red, Green and Blue components, respectively. An color image can be saved as an pure quaternion matrix, , in which an element, , denotes one color pixel, and , and are nonnegative integers [11].

Suppose that there are training color image samples in total, denoted as pure quaternion matrices, , and the mean image of all the training color images can be defined as follows

(2.2)

Model 1 (2dcpca).
Compute the following color image covariance matrix of training samples
(2.3)
Compute the largest eigenvalues of and their corresponding eigenvectors (called eigenfaces), denoted as . Let the eigenface subspace be . Compute the projections of training color face images in the subspace, ,
(2.4)
For a given testing sample, , compute its feature matrix, . Seek the nearest face image, , whose feature matrix satisfies that . is output as the person to be recognized.

2DCPCA based on quaternion models can preserve color and spatial information of face images, and its computational complexity of quaternion operations is similar to the computational complexity of real operations of 2DPCA (proposed in [23]).

3 Sample-relaxed 2DCPCA

In this section, we present a sample-relaxed 2DCPCA approach based on quaternion models for face recognition.

Suppose that all the color image samples of the training set can be partitioned into classes with each containing samples:

where represents the -th sample of the -th class. Now, we define a relaxation vector using label and variance within a class, and generate a sample-relaxed covariance matrix of training set in the following.

3.1 The relaxation vector

Define the mean image of training samples from the -th class by

and the -th within-class covariance matrix by

(3.1)

where and .

The within-class covariance matrix, , is a Hermitian quaternion matrix, and is semi-definite positive, with its eigenvalues being nonnegative. The maximal eigenvalue of , denoted as , represents the variance of training samples, , in the principal component. Generally, the larger is, the better scattered the training samples of the -th class are. If , all the training samples in the -th class are same, and the contribution of the -th class to the covariance matrix of the training set should be controlled by a small relaxation factor.

Now, we define a relaxation vector for the training classes.

DEfinition 3.1.

Suppose that the training set has classes and the covariance matrix of the -th class is . Then the relaxation vector can be defined as

(3.2)

where

is the relaxation factor of the -th class.

If the -th class contains training samples, the relaxation factor of each sample can be calculated as . If each training class has only one sample, i.e., , all the within-class covariance matrices will be zero matrices, and

In this case, the relaxation factor of each class will be , and so will its unique sample.

3.2 The relaxed covariance matrix and eigenface subspace

Now, we define the relaxed covariance matrix of training set.

DEfinition 3.2.

With the relaxation vector, , defined by (3.2), the relaxed covariance matrix of training set can be defined as follows

(3.3)

where means the number of training samples in the -th class, , and is defined by (2.2).

If there is no label information, or people are unwilling to use such information, the training set can be regarded as containing classes, with each having one sample, i.e., and . In this case, the relaxed covariance matrix, , defined by (3.3) is exactly the covariance matrix, , defined by (2.3).

DEfinition 3.3.

Suppose that is the relaxed covariance matrix of training set. The generalized total scatter criterion can be defined as follows

(3.4)

where is a quaternion matrix with unitary column vectors.

Note that is real and nonnegative since the quaternion matrix, , is Hermitian and positive semi-definite. Our aim is to select the orthogonal projection axes, , to maximize the criterion, , i.e.,

(3.5)

These optimal projection axes are in fact the orthonormal eigenvectors of corresponding to the largest eigenvalues.

Once the relaxed covariance matrix, , is built, we can compute its largest (positive) eigenvalues and their corresponding eigenvectors (called eigenfaces), denoted as . Let the optimal projection be

(3.6)

and the diagonal matrix be

(3.7)

Then and . Following that, for any quaternion matrix norm , we define the -norm as follows

3.3 Color face recognition

In this section, we apply the optimal projection, , and the positive definite matrix, , to color face recognition.

The projections of training color face images in the subspace, , are

(3.8)

where is defined by (2.2). The columns of , , , are called the principal component (vectors), and is called the feature matrix or feature image of the sample image, . Each principal component

of the sample-relaxed 2DCPCA is a quaternion vector. With the feature matrices in hand, we use a nearest neighbour classifier for color face recognition.

Now, we present a sample-relaxed two-dimensional color principal component analysis (SR-2DCPCA) approach.

Model 2 (Sr-2dcpca).
Compute the relaxed color image covariance matrix, , of training samples by (3.3) Compute the largest eigenvalues of and their corresponding eigenvectors (called eigenfaces), denoted as . Let the eigenface subspace be and the weighted matrix be . Compute the projections of training color face images in the subspace, ,
For a testing sample, , compute its feature matrix, . Seek the nearest face image, whose feature matrix satisfies that . is output as the person to be recognized.

SR-2DCPCA can preserve color and spatial information of color face images as 2DCPCA (proposed in [8]). Compared to 2DCPCA, SR-2DCPCA has an additional computation amount on calculating the relaxation vector, and generally provides a better discriminant for classification. The aim of the projection of SR-2DCPCA is to maximize the variance of the whole samples, while that of 2DCPCA is to maximize the variance of training samples. Note that the eigenvalue problems of Hermitian quaternion matrices in Model 2 can be solved by the fast structure-preserving algorithm proposed in [9].

Now we provide a toy example.

Example 3.1.

As shown in Figure 3.1, randomly generated points are equally separated into two classes (denoted as and , respectively). We choose 100 points from each class as training samples (denoted as magenta and ) and the rest as testing samples (denoted as blue and ). The principle component of training points is computed by 2DCPCA and SR-2DCPCA. In three random cases, the relaxation vectors are , and ; the computed principle components are plotted with the blue lines. The variances of the training set and the whole points, under the projection of 2DCPCA and SR-2DCPCA, are shown in the following table.

Case Variance of training points Variance of the whole points
2DCPCA SR-2DCPCA 2DCPCA SR-2DCPCA
Figure 3.1: Points in three random cases and the principle components (blue lines).

3.4 Color image reconstruction

Now, we consider color image reconstruction based on SR-2DCPCA. Without loss of generality, we suppose that . In this case, the projected color image is .

THeorem 3.1.

Suppose that is the unitary complement of  such that is a unitary quaternion matrix. Then the reconstruction of a training sample, , is , and

(3.9)
Proof.

We only need to prove (3.9). Since quaternion matrix is unitary,

where is an identity matrix. The projected image, , of the sample image, , satisfies that

Therefore, we have

According to [8], the image reconstruction rate of can be defined as follows

(3.10)

Since is generated based on eigenvectors corresponding to the largest eigenvalues of , is always a good approximation of the color image, . If the number of chosen principal components ,

is a unitary matrix and

, which means .

From the above analysis, SR-2DCPCA is convenient to reconstruct color face images from the projections, as 2DCPCA proposed in [8].

4 Variance updating

In this section we analysis the contribution of training samples from each class to the variance of the projected samples.

For the -th class with training samples, let

According to the definition of the relaxed covariance matrix (3.3), we can rewrite as

(4.1)

Denote all the eigenvalues of an -by- Hermitian quaternion matrix, , as in descending order.

LEmma 4.1.

Suppose that , where is Hermitian, and . Then

Proof.

The properties for the eigenvalues of Hermitian (complex) matrices [15, Theorem 3.8, page 42] are still right for Hermitian quaternion matrices. Suppose that are Hermitian matrices, then

(4.2)

for . Consequently,

(4.3)

Since

is Hermitian and semi-definite positive, its maximal singular value,

, is exactly its maximal eigenvalue, which is the multiplication of the maximal singular values of and . That is . From equation (4.3), we can obtain that

Since Hermitian quaternion matrices, , and are semi-definite positive, we obtain that

Let the variance of projections of all the training samples on be

The following theorem characterizes the contribution of training samples from one class to the variance of all the projected training samples.

THeorem 4.2.

With the above notations and a fixed relaxation vector , the variance of projections of all the training samples satisfies that

Proof.

From equation (4.1), we obtain that

Since Hermitian quaternion matrices, and , are semi-definite positive, we can see that

and Lemma 4.1 implies that

Thus

5 Experiments

In this section we compare the efficiencies of five approaches on color face recognition:

  • PCA: the Principle Component Analysis (with converting Color-image into Grayscale),

  • CPCA: the Color Principle Component Analysis proposed in [22],

  • 2DPCA: the Two-Dimensional Principle Component Analysis proposed in [23] (with converting Color-image into Grayscale),

  • 2DCPCA: the Two-Dimensional Color Principle Component Analysis proposed in [8],

  • SR-2DCPCA: the Sample-Relaxed Two-Dimensional Color Principle Component Analysis proposed in Section 3.

All the numerical experiments are performed with MATLAB-R2016 on a personal computer with Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.4GHz (dual processor) and RAM 32GB.

Example 5.1.

In this experiment, we compare SR-2DCPCA with PCA, CPCA, 2DPCA and 2DCPCA using the famous Georgia Tech face database [26]. The database contains various pose faces with various expressions on cluttered backgrounds. All the images are manually cropped, and then resized to pixels. Some cropped images are shown in Fig. 5.1.

Figure 5.1: Sample images for one individual of the Georgia Tech face database.

The first ( or ) images of each individual are chosen as the training set and the remaining as the testing set. The numbers of the chosen eigenfaces are from to . The face recognition rates of five PCA-like methods are shown in Fig. 5.2. The top and the below figures show the results for cases that the first and face images per individual are chosen for training, respectively.

The maximal recognition rate (MR) and the CPU time for recognizing one face image in average are listed in Table 1. We can see that SR-2DCPCA reaches the highest face recognition rate, and costs less CPU time than CPCA.

Figure 5.2: Face recognition rate of PCA-like methods for the Georgia Tech face database.
CPCA 2DPCA 2DCPCA SR-2DCPCA
MR CPU MR CPU MR CPU MR CPU
7.58 0.02 0.09 0.85
23.80 0.06 0.30 2.16
Table 1: Maximal face recognition rate and average CPU time (milliseconds)
Example 5.2.

In this experiment, we compare three two-dimensional approaches: 2DPCA, 2DCPCA and SR-2DCPCA on face recognition and image reconstruction, by using the color Face Recognition Technology (FERET) database [27]. The database (version 2, DVD2, thumbnails) contains 269 persons, 3528 color face images, and each person has various numbers of face images with various backgrounds. The minimal number of face images for one person is 6, and the maximal one is 44. The size of each cropped color face image is pixels, and some samples are shown in Fig. 5.3.

Figure 5.3: Sample images of the color FERET face database.

Taking the first 6 images of each individual as an example, we randomly choose image/images as the training set and the remaining as the testing set. This process is repeated 5 times, and the average value is output. For a fixed , we consider ten cases that the number of the chosen eigenfaces increases from to . The average face recognition rate of these ten cases is shown in the top of Fig. 5.4. When , the face recognition rate with the number of eigenfaces increasing from 1 to 10 is shown in the below of Fig. 5.4.

Figure 5.4: Face recognition rate of PCA-like methods for the color FERET face database.

With , we also employ 2DCPCA and SR-2DCPCA to reconstruct the color face images () in training set from their projections (). In Fig. 5.5, the reconstruction of the first four persons with 2, 10, 20, 38 and 128 eigenfaces: the top and the below pictures are reconstructed by 2DCPCA and SR-2DCPCA, respectively. In Fig. 5.6, we plot the ratios of image reconstruction by 2DCPCA (top) and SR-2DCPCA (below) with the number of eigenfaces changing from 1 to 128. The difference in the ratio between the two method is shown in Fig. 5.7 (the ratio of SR-2DCPCA subtracts that of 2DCPCA). These results indicate that both 2DCPCA and SR-2DCPCA are convenient to reconstruct color face images from projections, and can reconstruct original color face images with choosing all the eigenvectors to span the eigenface subspace.

Figure 5.5: Reconstructed images by 2DCPCA (top) and SR-2DCPCA (below).
Figure 5.6: Ratio of the reconstructed images over the original face images.
Figure 5.7: The image reconstruction ratio of SR-2DCPCA subtracted by that of 2DCPCA.

6 Conclusion

In this paper, SR-2DCPCA is presented for color face recognition and image reconstruction based on quaternion models. This novel approach firstly applies label information of training samples, and emphasizes the role of training color face images with the same label which have a large variance. The numerical experiments indicate that SR-2DCPCA has a higher face recognition rate than state-of-the-art methods and is effective in image reconstruction.

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