使用模块化PCA方法改进面部识别技术毕业论文外文翻译.doc
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1、附 录一、英文原文An improved face recognition technique basedon modular PCA approachRajkiran Gottumukkal Vijayan K.AsariAbstractA face recognition algorithm based on modular PCA approach is presented in this paper. The proposed algorithm when compared with conventional PCA algorithm has an improved recognit
2、ion rate for face images with large variations in lighting direction and facial expression. In the proposed technique, the face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. Since some of the local facial features of an individual do not vary
3、 even when the pose, lighting direction and facial expression vary, we expect the proposed method to be able to cope with these variations. The accuracy of the conventional PCA method and modular PCA method are evaluated under the conditions of varying expression, illumination and pose using standar
4、d face databases.Keywords: PCA; Face recognition; Modular PCA; Pose invariance; Illumination invariance1. IntroductionFace recognition is a difficult problem because of the generally similar shape of faces combined with the numerous variations between images of the same face. The image of a face cha
5、nges with facial expression, age, viewpoint, illumination conditions, noise etc. The task of a face recognition system is to recognize a face in a manner that is as independent as possible of these image variations.Automatic recognition of faces is considered as one of the fundamental problems in co
6、mputer vision and pattern analysis, and many scientists from different areas have addressed it. Chellappa et al. (1995) presented a survey on several statistical-based, neural network-based and feature-based methods for face recognition. Currently, one of the methods that yields promising results on
7、 frontal face recognition is the principal component analysis (PCA),which is a statistical approach where face images are expressed as a subset of their eigenvectors, and hence called eigenfaces (Sirovich and Kirby,1987; Turk and Pentland,1991;Moghaddam and Pentland,1997; Martinez, 2000; Graham and
8、Allinson,1998).PCA has also been used for handprint recognition (Murase et al.,1981), human-made object recognition (Murase and Nayar, 1995), industrial robotics (Nayar et al.,1996), and mobile robotics (Weng,1996).But results show that the recognition rate is not satisfactory for pose variations ex
9、ceeding 30 and extreme changes in illumination.The main objective of this research is to improve the accuracy of face recognition subjected to varying facial expression, illumination and head pose. As stated before, PCA method has been a popular technique in facial image recognition.But this techniq
10、ue is not highly accurate when the illumination and pose of the facial images vary considerably. In this research work an attempt is made to improve the accuracy of this technique under the conditions of varying facial expression, illumination and pose. We propose the modular PCA method, which is an
11、 extension of the conventional PCA method. In the modular PCA method the face images are divided into smaller images and the PCA method is applied on each of them. Whereas in the traditional PCA method the entire face image is considered, hence large variation in pose or illumination will affect the
12、 recognition rate profoundly. Since in the case of modular PCA method the original face image is divided into sub-images the variations in pose or illumination in the image will affect only some of the sub- images, hence we expect this method to have better recognition rate than the conventional PCA
13、 method. A similar method called modular eigenspaces was proposed by Pentland et al. (1994).In this method PCA is performed on the eyes and nose of the face image.This paper is organized as follows: Section 2 describes the conventional PCA method. Section 3 explains the modular PCA method. Section 4
14、 describes the face databases used for testing the face recognition methods. Section 5 presents simulation results obtained by applying the PCA method and the proposed modular PCA method to the face image sets with large light and pose variations. Finally, a conclusion is drawn in Section 6.2. Revie
15、w of the PCA methodThe PCA method has been extensively applied for the task of face recognition. Approximate reconstruction of faces in the ensemble was per- formed using a weighted combination of eigenvectors (eigenpictures), obtained from that ensemble (Sirovich and Kirby, 1987).The weights that c
16、haracterize the expansion of the given image in terms of eigenpictures are seen as global facial features. In an extension of that work, Kirby and Sirovich (1990) included the inherent symmetry of faces in the eigenpictures.All the face images in the face database are represented as very long vector
17、s, instead of the usual matrix representation. This makes up the entire image space where each image is a point Since the faces have a similar structure (eye, nose and mouth, position, etc.), the vectors representing them will be correlated. We will see that faces of the same class will group at a c
18、ertain location in the image space. Hence the face images are rep resented by a set of eigenvectors developed from a covariance matrix formed by the training of face images. The idea behind eigenimages (in our case eigenfaces) is to find a lower dimensional space in which shorter vectors will descri
19、be face images.Fig.1 illustrates this idea graphically.2.1. Computing eigenfacesConsider the face images in the face database to be of size L by L. These images can be represented as a vector of dimension L2 ,or a point in L2dimensional space. A set of images therefore corresponds to a set of points
20、 in this high dimensional space. Since facial images are similar in structure, these points will not be randomly distributed, and therefore can be described by a lower dimensional subspace.PCA gives the basis vectors for this subspace (which is called the face space).Each basis vector is of length L
21、2 , and is the eigenvector of the covariance matrix corresponding to the original face images.Let be the training set of face images. The average face is defined by (1)Each face differs from the average face by the vector.The covariance matrix C is obtained as (2)The eigenvectors of the covariance m
22、atrix are computed and the significant eigenvectors are chosen as those with the largest corresponding eigenvalus. From these eigenvectors, the weights for each image in the training set are computed as (3)Where s are the eigenvectors corresponding to thelargest eigenvalues of C and K varies from 1
23、to.2.2 ClassicationA test image Itest is projected into face space by the following operation: (4)pThe weights form a vector, which describes the contribution of each eigenface in representing the input face image. This vector can then be used to fit the test image to a predefined face class. A simp
24、le technique is to compute distance of from, where Tp is the mean weight vector of the pth class. The test image can be classified to be in class p when, where and is the threshold.3. Modular PCA methodThe PCA based face recognition method is not very effective under the conditions of varying pose a
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