Robust 2DPCA and Its Application

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.

Dangerous persons can be monitored accurately and quickly using the high-precision recognition of the relative facial images via the image sensors. To increase the recognition rate of face recognition by improving the immune algorithm, the immune computation was redesigned for better face recognition to decrease the facial disturbances of the pose, illumination and expression (PIE), in this paper. The clonal selection algorithm was improved with the modification of the affinity and the algorithm workflow. The improved clonal selection algorithm searches the most similar antibody sample against the antigen of an unknown facial image, according to the affinity between the antigen and the antibody. The unknown facial images were recognized with this improved affinity and the uncertainty-based reasoning, so the affinity matching of the antibody with the unknown antigen was also improved. Experimental results show that this immune algorithm outperforms some state-of-the-art algorithms in the face recognition accuracy tests with such facial image databases as AR, Yale and CMU-PIE. So the proposed immune algorithm is useful and effective to improve the performance of face recognition in the image sensor network.

In spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes, growing of one’s beard, wearing of glasses, and other forms of occlusions can hardly hinder the power of the human brain from making a face recognition. However, the same cannot easily be said about automated intelligent systems which have been developed to mimic the skill of the human brain to aid in recognition. There have been growing interests in developing a resilient and efficient recognition system mainly because of its numerous application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). Although there have been numerous researches on face recognition under varying pose, illumination, expression, and image degradations, problems caused by occlusions are mostly ignored. This study thus focuses on facial occlusions and proposes an enhancement mechanism through face image augmentation to improve the recognition of occluded face images. This study assessed the performance of Principal Component Analysis with Singular Value Decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing face recognition algorithm on face images with missingness and augmented face image database. It was found that the average recognition rates for the FFT-PCA/SVD algorithm were the same ( 90 % ) when face images with missingness and augmented face images were used as test images, respectively. The statistical evaluation revealed that there exists a significant difference in the average recognition distances for the face images with missingness and augmented face images when FFT-PCA/SVD is used for recognition. Augmented face images tend to have a relatively lower average recognition distance when used as test images. This finding is contrary to the equal performance assessment by the adopted numerical technique. The MICE algorithm is therefore recommended as a suitable imputation mechanism for enhancing/improving the performance of the face recognition system.

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 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)

In recent days, there has been increasing need for recognition of unconstrained face images, such as those collected from the web or captured by mobile devices and video surveillance cameras. In such real world scenarios, human faces could be easily occluded by other objects that make the face recognition task as a complex one. Satisfactory performance has been achieved earlier but often only in the controlled environments. But it is very tedious to obtain holistic face images for unconstrained face recognition. Thus, in order to avoid the degradation of face images and the huge variations due to illumination, pose, occlusion and expression, a new Robust Face Recognition approach is proposed. In this approach, the partial face recognition using Scale Invariant Feature Transform (SIFT) technique is combined with Multi-directional Multi-level Dual Cross Patterns (DCP) technique that makes the recognition task as a robust one when compared to other face recognition approaches. Then, the Robust Point Set Matching (RPSM) is used to match the corresponding stable keypoints from both the gallery image and probe face image. Finally, PNN and K-NN classification are used to classify the face images even with the presence of occlusion, random partial crop, illumination, pose and exaggerated facial expression. The proposed robust face recognition system is evaluated based on the performance parameters such as sensitivity, specificity, accuracy, precision and recall.

In this paper, the face image recognition in the application of MATLAB to preprocess the image, application the toolbox classic image processing of the image, through the example to apply MATLAB image processing function, of a given face image processing, and applied to the face recognition system. In this paper, in summing up the face recognition system of analysis of several commonly used image pre processing method based, using MATLAB to realize a set a variety of pre processing method in one of the generic face image pre processing simulation system, the system as the image pre processing module can be embedded in the face recognition system, and the use of image gray histogram Alignment to achieve the recognition of human face images.

In this paper, a person-specific facial expression recognition method which is based on Personal Facial Expression Space (PFES) is presented. The multidimensional scaling maps facial images as points in lower dimensions in PFES. It reflects personality of facial expressions as it is based on the peak instant of facial expression images of a specific person. In constructing PFES for a person, his/her whole normalized facial image is considered as a single pattern without block segmentation and differences of 2-D DCT coefficients from neutral facial image of the same person are used as features. Therefore, in the early part of the paper, separation characteristics of facial expressions in the frequency domain are analyzed using a still facial image database which consists of neutral, smile, anger, surprise and sadness facial images for each of 60 Japanese males (300 facial images). Results show that facial expression categories are well separated in the low frequency domain. PFES is constructed using multidimensional scaling by taking these low frequency domain of differences of 2-D DCT coefficients as features. On the PFES, trajectory of a facial image sequence of a person can be calculated in real time. Based on this trajectory, facial expressions can be recognized. Experimental results show the effectiveness of this method.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper investigates a retrieval-based annotation paradigm of mining web facial images for automated face annotation. In general, there are two key challenges for such an annotation paradigm. The first challenge is how to efficiently retrieve a short list of most similar facial images from facial image databases, and the second challenge is how to effectively perform annotation by exploiting these similar facial images and their weak labels which are often noisy and incomplete. In this paper, we mainly focus on tackling the second challenge of the retrieval-based face annotation paradigm. In particular, we propose an effective Weak Label Regularized Local Coordinate Coding (WLRLCC) technique, which exploits the local coordinate coding principle in learning sparse features, and at the same time employs the graph-based weak label regularization principle to enhance the weak labels of the short list of similar facial images. We present an efficient optimization algorithm to solve the WLRLCC task, and develop an effective sparse reconstruction scheme to perform the final face name annotation. We conduct a set of extensive empirical studies on a large-scale facial image database withatotal of 6,000 persons and over 600,000 web facial images, in which encouraging results show that the proposed WLRLCC algorithm significantly boosts the performance of the regular retrieval-based face annotation approaches.

Dimensionality reduction methods play an important role in face recognition. Principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference (CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when 2DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.

Illumination variation significantly affects the performance of face recognition algorithms. However, it is difficult to construct a database that meets the demand of illumination analysis with current face image database construction technology. The influence of illumination variation on deep face recognition based on 3D face models is investigated. First a new method is presented to produce face images under arbitrary illuminations by using a 3D face model based on face basis image representation theory proposed. Then the database used for illumination analysis is constructed. Afterwards, an optimal way is explored to select illuminations used for constructing a high quality face image database by analyzing the impacts of different illumination sampling strategies on the performances of face recognition models. With the advantage of accurate illumination labeling of virtual data, the performances of different illumination labeling methods on the training of face recognition network are tested on a multi-task learning framework. Through experiments on the effects of different lighting sampling schemes and labeling methods on the performances of face recognition models carried out on virtual data and real data, it is concluded that using an appropriate amount of lightings of basis images to build a database is an effective lighting sampling scheme, and accurate lighting annotation can help further improve the face recognition rate, the corresponding recognition models have face recognition accuracy rates of more than 98% on virtual and real monitoring datasets which contain face images captured under very challenging lighting conditions. Proposed work improve the performance of deep face recognition models, and facilitate the construction of high quality face image databases with various illuminations.

The objective of this project was to assess the performance of the leading commercial-off-the-shelf (COTS) facial recognition software package when used as a laptop application. We performed the assessment to determine the system's usefulness for enrolling facial images in a database from remote locations and conducting real-time searches against a database of previously enrolled images. The assessment involved creating a database of 40 images and conducting 2 series of tests to determine the product's ability to recognize and match subject faces under varying conditions. This report describes the test results and includes a description of the factors affecting the results. After an extensive market survey, we selected Visionics' FaceIt{reg_sign} software package for evaluation and a review of the Facial Recognition Vendor Test 2000 (FRVT 2000). This test was co-sponsored by the US Department of Defense (DOD) Counterdrug Technology Development Program Office, the National Institute of Justice, and the Defense Advanced Research Projects Agency (DARPA). Administered in May-June 2000, the FRVT 2000 assessed the capabilities of facial recognition systems that were currently available for purchase on the US market. Our selection of this Visionics product does not indicate that it is the ''best'' facial recognition software package for all uses. It was the most appropriate package based on the specific applications and requirements for this specific application. In this assessment, the system configuration was evaluated for effectiveness in identifying individuals by searching for facial images captured from video displays against those stored in a facial image database. An additional criterion was that the system be capable of operating discretely. For this application, an operational facial recognition system would consist of one central computer hosting the master image database with multiple standalone systems configured with duplicates of the master operating in remote locations. Remote users could perform real-time searches where network connectivity is not available. As images are enrolled at the remote locations, periodic database synchronization is necessary.

This paper proposes a two-dimensional color uncorrelated principal component analysis algorithm(2DCUPCA) for unsupervised subspace learning directly from color face images. The 2DCUPCA can be used to explore uncorrelated properties among color-based features, which contain minimum redundancy and ensure linear independence among features. Furthermore, the proposed 2DCUPCA provided the theoretical foundations analysis and proved the uncorrelated property between color-based features in theory. This makes it sure that the extracted features directly from three color image matrices will be uncorrelated. Finally, experimental results on the AR and FRGC-2 color face databases show that 2DCUPCA achieves better recognition performance than other color face recognition methods.KeywordsPattern recognitionface recognitionuncorrelated principal component analysisfeature extraction