Two-Dimensional Color Uncorrelated Principal Component Analysis for Feature Extraction with Application to Face Recognition

Color face image is composed of color information of different channels. Color image provides more information for face recognition task compared with grey scale image. A novel approach coined block diagonal non-negative matrix factorization (BDNMF) is proposed for color face representation and recognition. The approach employs block diagonal matrix to encode color information of different channels. Block diagonal constraint is imposed on the non-negative matrix factorization algorithm to factorize matrices of different channels simultaneously. And block diagonal non-negative matrix factorization algorithm is exploited to extract facial features. Nearest neighborhood classifier is adopted to identify color face samples. Experimental results on CVL and CMU PIE color face databases verify the effectiveness of the proposed approach.

Although two-dimensional principal component analysis (2DPCA) extracts image features directly from 2D image matrices rather than one dimensional vectors, 2DPCA is only based on the whole images to preserve total variances by maximizing the trace of feature covariance matrix. Thus, 2DPCA cannot extract localized components, which are usually important for face recognition. Inspired by nonnegative matrix factorization (NMF), which is based on localized features, we propose a novel algorithm for face recognition called two-dimensional nonnegative principal component analysis (2DNPCA) to extract localized components and maintain the maximal variance property of 2DPCA. 2DNPCA is a matrix-based algorithm to preserve the local structure of facial images and has the nonnegative constraint to learn localized components. Therefore, 2DNPCA has both advantages of 2DPCA and NMF. Furthermore, 2DNPCA solves the time-consuming problem by removing the restriction of minimizing the cost function and extracting only the base matrix. The nearest neighbor (NN) classifier and linear regression (LR) classifier are used for classification and extensive experimental results show that 2DNPCA plus NN and 2DNPCA plus LR are both very efficient approaches for face recognition.

As an active research topic, many algorithms have been presented for face recognition. However, they mainly utilize the monochromatic intensity information. Among a few color face recognition methods, most of them treat the three channels separately. In this paper, a color face image is treated in a holistic manner by using the quaternion theory. We then propose a new algorithm for color face recognition, which uses the quaternion Zernike moment invariants and the quaternion BP neural network for the color face recognition. Experimental results on the Collection of Facial Images (Grimace) database, including major expression variation and considerable variation in head turn and tilt, show that the proposed method is better than the conventional ones in recognition rate.

In Brief

This paper presents a novel color face recognition algorithm by means of fusing color and local information. The proposed algorithm fuses the multiple features derived from different color spaces. Multiorientation and multiscale information relating to the color face features are extracted by applying Steerable Pyramid Transform (SPT) to the local face regions. In this paper, the new three hybrid color spaces, YSCr, Z n SCr, and B n SCr, are firstly constructed using the Cb and Cr component images of the Y Cb Cr color space, the S color component of the HSV color spaces, and the Z n and B n color components of the normalized XYZ color space. Secondly, the color component face images are partitioned into the local patches. Thirdly, SPT is applied to local face regions and some statistical features are extracted. Fourthly, all features are fused according to decision fusion frame and the combinations of Extreme Learning Machines classifiers are applied to achieve color face recognition with fast and high correctness. The experiments show that the proposed Local Color Steerable Pyramid Transform (LCSPT) face recognition algorithm improves seriously face recognition performance by using the new color spaces compared to the conventional and some hybrid ones. Furthermore, it achieves faster recognition compared with state-of-the-art studies.

Face recognition is an existing and one of the most prominent biometrics techniques, including the processing of images. It is widely used in many applications. The performance of such systems is directly due to face image quality. Since blur and motion blur are common imagery problems, this paper explores the influence of such disturbances on color face recognition performance. The research described in this paper compares the performance of the face recognition algorithm based on the Haar features and Local Binary Patterns Histograms when it uses color face images of good quality, images with added Gaussian blur and motion blur, as well as enhanced images.

Two-dimensional feature extraction methods such as two-dimensional principal component analysis (2DPCA) and two-dimensional linear discriminant analysis (2DLDA) have been extensively studied in the past several years. Numerous experimental results demonstrate that these two-dimensional feature extraction methods are generally more efficient than and at least as effective as their one-dimensional counterparts in face recognition. However, in contrary to the large number of studies in two-dimensional feature extraction methods, studies in two-dimensional pattern classification are quite few. In this paper we propose two kinds of two-dimensional nearest neighbor classifiers and test their performance in face recognition. Extensive experimental studies conducted on four benchmark face image databases: OR, Yale, FERET, and AR demonstrate that the proposed classifiers can achieve higher recognition accuracies than the nearest neighbor classifier in general.

In this paper, based on the study of the Two-Dimensional Principal Component Analysis (2DPCA), Two-Dimensional Principal Component Analysis (2DPCA) and fuzzy set theory, we propose a integrated face recognition algorithm based on wavelet subspace. This method can make good use of the advantages of each single method, and also can make up for the defect of each other. The comparison of the results of the different methods identification effect on the ORL、YALE and FERET face database show, the integrated method proposed in this paper improves the recognition rate, and it also reduces the training and classification time as well. Key word: face recognition; Two-Dimensional Principal Component Analysis (2DPCA); Two-Dimensional Linear Discriminant Analysis (2DLDA); fuzzy set theory; feature extraction be divided into several categories:the method based on geometric features、the method based on model、the method based on the statistical、the method based on neural network method and the method of combining multiple classifiers, this paper mainly focuses on the method based on statistics. The method based on statistical faced the image as random vector, thus using some statistical methods to analyze the face model, the most representative methods include the eigenface method based on principal component analysis which proposed by Turk M (2)

This paper proposes a novel face super-resolution reconstruction (hallucination) technique, color face images reconstruction of RGB space with an error regression model in multi-linear principal component analysis (MPCA). From hallucination framework, many color face images are explained in RGB space. Then, they can be naturally described as tensors or multi-linear arrays. In this way, the error regression analysis is used to find the error estimation which can be obtained from the existi ng LR in tensor space. The framework consists of learning and hallucinating process. In learning process is from the mistakes in reconstruct face images of the training dataset by MPCA, then finding the relationship between input and error by regression analysis. In hallucinating process uses normal method by back-projection of MPCA, after that the result is corrected with the error estimation. In this contribution we show that our hallucination technique can be suitable for color face images both in RGB space. By using the MPCA subspace with error regression model, we can generate photorealistic color face images. Our approach is demonstrated by extensive experiments with high-quality hallucinated color faces. In addition, our experiments on face images from FERET database validate our algorithm

In this paper, based on face recognition, the core algorithm is studied, which was applied in access control system. This algorithm carries on two-dimensional principal component analysis (2DPCA), singular value decomposition (SVD) and the fusion of 2DPCA and SVD, which the main characteristic vector of the PCA is fused to form a new one that is taken as the distinction standard. The experiment results, by means of ORL and Yale face database, the recognition rate is up to 87% and 98.9%, respectively. The effectiveness of the proposed face recognition algorithm and its insensitivity to the facial expression, illumination and posture are shown in terms of both the absolute performance indices and the comparative performance against some popular face recognition schemes such as singular value decomposition based method.

Advanced variations of two-dimensional principal component analysis for face recognition

The traditional two-Dimensional Principal Component Analysis(2DPCA) only extracts the in-line features of data of face image, the direction of feature extraction is relatively simple, and the feature extraction in other directions is not considered. In order to extract the features of the image from multiple angles and provide more abundant information for recognition, a new method of 2DPCA face recognition is proposed. The new algorithm first self-corrects the face image, at the same time, it extracts the low frequency information of the image, and then it uses the Perceptual hash technique to obtain the ‘fingerprint’ of the image. Then, the new algorithm will rotate multi-angle images from the self-corrected face images and extract the features separately to get multi-angle feature information. Finally, the training sample pictures are classified again for each category, and the images of similar expressions or features are classified to retain the special expressions or features. The numerical experiments in the ORL human face databases show that the improved algorithm is superior to the traditional 2DPCA algorithm.

In this paper, the improved face recognition method based on two-directional 2DPCA (two-dimensional principal component analysis) in each block of face images is proposed. Firstly, the face image is divided into several sub-images, and then the sub-image features of each corresponding block are extracted by two-directional 2DPCA according to the number of sub-images. Finally, using the support vector machine to improve the recognition rate. Experimental results on ORL face database and YALE face database show that the proposed method is superior to any other 2DPCA methods in face recognition rate.

Two-dimensional principal component analysis (2D-PCA) which is based on 2D image matrices as opposed to the standard PCA, which is based on 1D vectors, has been first successfully applied to face recognition and has higher accuracy than the latter. It was also successfully applied to other problems such as facial expression recognition, object recognition, etc. later. However, there exists still no neural network learning algorithm for 2D-PCA like those for PCA yet. In this paper, we propose a learning algorithm for 2D-PCA. Requiring no image covariance matrix evaluation and just repeatedly presenting training image samples to the single layer neural network, the desired multiple eigenvectors for 2D-PCA can be learned in the form of weight vectors of generalized linear neurons. It also profit from the parallel architecture of neural network. Simulation experiments are performed on YaleB face database, and the experimental results show that the proposed learning algorithm performs well as expected.

The ionospheric total electron content (TEC) data from 05 to 10 January 2013 (UT) were examined by two-dimensional principal component analysis (2DPCA) and revealed a TEC anomaly related to the volcanic eruption in Colima, Mexico on 06 January, 2013. A TEC anomaly was detected above the volcano from 21:00 to 21:05 on 06 January 2013 (UT). The anomaly occurred for at least 5 minutes, during this time period we could identify the equatorial ionization anomaly (EIA). The shock acoustic wave produced by eruption-induced seismic activity may be the cause of the observed TEC anomaly. We show that it is possible to monitor or predict volcanic eruptions and their associated seismic activity through the use of two-dimensional principal component analysis of the resulting TEC anomalies.