Elastic Bunch Graph Matching Research Articles

New Extracted Features to Recognize Faces Effected by Occlusions and Common Variations

Face recognition from non-identical face photos is a prominent area of research in pattern recognition and computer vision. Existing face recognition systems struggle with diverse changes like lighting conditions, expressions, and facial occlusions. This paper proposes a new Face Recognition (FR) approach that combines the Elastic Bunch Graph Matching (EBGM) approach with the greedy algorithm to automatically identify face landmarks. The proposed approach independently selects each optimal landmark of face image from different corresponding face images where the corresponding landmark of corresponding face image which achieves the best similarity is used rather than using one or at most two corresponding face images and computing the average between both. The locations of corresponding landmarks can be displaced to achieve maximum similarity with optimal landmarks. This proposed approach demonstrates improved recognition performance compared to contemporary face recognition methods. It effectively handles changing ratios of face parts and can recognize faces even with increasing occlusion sizes.

Analytical Study of Face Recognition Methods Using Machine Learning

Many facial recognition algorithms, as well as their adaptations, have previously been devised during the last decades. A number is indicated for standard algorithms. They are divided into two categories: appearance-based and model-based. Three distinct linear subspace analysis strategies for appearance-based processes are presented, along with a brief examination of numerous non-linear manifold analysis approaches for face recognition. Elastic Bunch Graph matching, Active Appearance Model, and 3D Model processes are discussed. A vast variety of publicly available face datasets and performance evaluation ratings are examined. Based on previous recognition outcomes, more study areas are recommended. Face recognition has piqued the interest of academics in biometrics, pattern recognition, and computer vision. Previous research in this field had a basic fault in that it was designed to recognize just single faces, resulting in a long detection time and additional time for annotation. The goal of this study is to create a face recognition system that makes use of a machine learning algorithm and principal component analysis (PCA). The study makes use of linear discriminant analysis, multilayer perceptrons, Naive Bayes, and support vector machines. Furthermore, it obtained 97% and 100% identification accuracy, respectively, using PCA and linear discriminant analysis. The extensive comprehension CNNs are the most frequent form of machine learning algorithm used for facial recognition. CNNs are a sort of artificial neural network that performs exceptionally well at image categorization. Face recognition is a popular topic in artificial intelligence. This program was widely used in our daily life. Several face recognition devices were installed on phones to protect private information and utilized on Facebook to quickly recognize when Facebook users appeared in photographs. Several face recognition algorithms have previously been developed; yet, it remains incredibly difficult in real-world situations. A basic technique for differentiating people is influenced by factors such as partial facial occlusion, illumination, and posture variance.

Review on various face recognition databases

Face recognition is one of the multimedia items that has seen a remarkable increase in popularity in recent years. Face continues to be the most difficult study topic for experts in the field of computer vision and image processing since it is an item with different properties for detection. We have attempted to handle the most challenging facial aspects in this survey work, including posture invariance, aging, illuminations, and partial occlusion. When applied to facial photographs, they are regarded as essential components of face recognition systems. The most recent face detection methods and techniques are also examined in this paper, including Eigenface, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, Elastic Bunch Graph Matching, 3D Morphable Models, and Hidden Markov Models. Many testing face databases, such as AT & T (ORL), AR, FERET, LFW, YTF, and Yale, also reviewed. However, the purpose of this study is to present a thorough literature assessment on face recognition and its applications.

Algorithms for face recognition drones

This paper elucidates diverse algorithms that are used in drones for face identification. Face identification is the thriving technology that utilizes image processing for the identification of human faces. The main reasons for the booming interest in face recognition include the rise in population which demands high security and surveillance systems, requisite for identity verification in the digital world, for the combat in rural areas, alleviation during disasters, and so on. The goal of this paper is to explore, compare and contrast numerous face identification algorithms such as Linear Discriminant Analysis (LDA), Local Binary Pattern Histogram (LBPH), Principal Component Analysis (PCA), Elastic Bunch Graph Matching (EBGM) and neural networks. This study would assist the technologists in the field of face recognition to frame a hybrid algorithm based on the needs of the real time applications.

Eigenfaces Technique, an Improved Face Recognition Approach Using Neural Network

Image analysis and understanding, stands tall amongst all the technologies and face recognition is an eminent part of it. A face database is maintained as a logbook to identify an input face. This is accomplished by mere comparison amongst the face database. There are several face recognition techniques, of which, symmetry, Elastic Bunch Graph Matching (EBGM), and analytic-to-holistic recognition have been explored in this research paper. Other peculiar approaches like image based face recognition techniques like MLP, convolutional neural network, eigenfaces, associative neural networks, recirculation neural network and independent component analysis have been thoroughly discussed. Two vibrant face recognition databases, UMIST and ORL have proved to be extremely important in analyzing the results of face recognition. Eigen Face value approach has been anticipated with the associated analysis of results of face recognition. Another approach in face recognition is optimized multiperceptron, which will be acting as the reference to the optimized eigenfaces approach in this research paper, hence making this study more efficient through comparison.

Study of Face Recognition Techniques: A Survey

With the rapid growth in multimedia contents, among such content face recognition has got much attention especially in past few years. Face as an object consists of distinct features for detection; therefore, it remains most challenging research area for scholars in the field of computer vision and image processing. In this survey paper, we have tried to address most endeavoring face features such as pose invariance, aging, illuminations and partial occlusion. They are considered to be indispensable factors in face recognition system when realized over facial images. This paper also studies state of the art face detection techniques, approaches, viz. Eigen face, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, Elastic Bunch Graph Matching, 3D morphable Model and Hidden Markov Models. In addition to the aforementioned works, we have mentioned different testing face databases which include AT & T (ORL), AR, FERET, LFW, YTF, and Yale, respectively for results analysis. However, aim of this research is to provide comprehensive literature review over face recognition along with its applications. And after in depth discussion, some of the major findings are given in conclusion.

Elastic Bunch Graph Matching Based Face Recognition Under Varying Lighting, Pose, and Expression Conditions

In this paper performance of elastic bunch graph matching (EBGM) for face recognition under variation in facial expression, variation in lighting condition and variation in poses are given. In this approach faces are represented by labelled graphs. Experimental results of EBGM on ORL, Yale B and FERET datasets are provided. Strong and weak features of EBGM algorithm are discussed.

A connectionist computational method for face recognition

Abstract In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.

Recognizing faces prone to occlusions and common variations using optimal face subgraphs

An intuitive graph optimization face recognition approach called Harmony Search Oriented-EBGM (HSO-EBGM) inspired by the classical Elastic Bunch Graph Matching (EBGM) graphical model is proposed in this contribution. In the proposed HSO-EBGM, a recent evolutionary approach called harmony search optimization is tailored to automatically determine optimal facial landmarks. A novel notion of face subgraphs have been formulated with the aid of these automated landmarks that maximizes the similarity entailed by the subgraphs. For experimental evaluation, two sets of de facto databases (i.e., AR and Face Recognition Grand Challenge (FRGC) ver2.0) are used to validate and analyze the behavior of the proposed HSO-EBGM in terms of number of subgraphs, varying occlusion sizes, face images under controlled/ideal conditions, realistic partial occlusions, expression variations and varying illumination conditions. For a number of experiments, results justify that the HSO-EBGM shows improved recognition performance when compared to recent state-of-the-art face recognition approaches.

Recognition of facial expressions based on salient geometric features and support vector machines

Facial expressions convey nonverbal cues which play an important role in interpersonal relations, and are widely used in behavior interpretation of emotions, cognitive science, and social interactions. In this paper we analyze different ways of representing geometric feature and present a fully automatic facial expression recognition (FER) system using salient geometric features. In geometric feature-based FER approach, the first important step is to initialize and track dense set of facial points as the expression evolves over time in consecutive frames. In the proposed system, facial points are initialized using elastic bunch graph matching (EBGM) algorithm and tracking is performed using Kanade-Lucas-Tomaci (KLT) tracker. We extract geometric features from point, line and triangle composed of tracking results of facial points. The most discriminative line and triangle features are extracted using feature selective multi-class AdaBoost with the help of extreme learning machine (ELM) classification. Finally the geometric features for FER are extracted from the boosted line, and triangles composed of facial points. The recognition accuracy using features from point, line and triangle are analyzed independently. The performance of the proposed FER system is evaluated on three different data sets: namely CK+, MMI and MUG facial expression data sets.

Human Detection and Face Recognition Using 3D Structure of Head and Face Surfaces Detected by RGB-D Sensor

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/11.jpg"" width=""300"" /> Summary of proposed method</div>Home service robots must possess the ability to communicate with humans, for which human detection and recognition methods are particularly important. This paper proposes methods for human detection and face recognition that are based on image processing, and are suitable for home service robots. For the human detection method, we combine the method proposed by Xia et al. based on the use of head shape with the results of region segmentation based on depth information, and use the positional relations of the detected points. We obtained a detection rate of 98.1% when the method was evaluated for various postures and facing directions. We demonstrate the robustness of the proposed method against postural changes such as stretching the arms, resting the chin on one’s hands, and drinking beverages. For the human recognition method, we combine the elastic bunch graph matching method proposed by Wiskott et al. with Face Tracking SDK to extract facial feature points, and use the 3D information in the deformation computation; we obtained a recognition rate of 93.6% during evaluation.

Comparison of RBFNN, FBNN and KNN Algorithms for Face Recognition using PCA and Rectangular Feature

Face Recognition is a popular application of computer vision in recent years. Not only the computer science researchers, but also the psychologists and neuroscientists are involved in this area. Appearance-based face recognition system includes linear Analysis like PCA, ICA, LDA, and Non-linear analysis is Model-based face recognition like, Elastic Bunch Graph Matching, 2D Morphable Model, 3D Morphable Model etc. In linear Analysis, matching score between the test face image and training images can be achieved by calculating the differences between their projection vectors determined by PCA, ICA or LDA. In nonlinear analysis, a mapping function is required to be applied on data space and then the linear analysis on the mapped data is applied. In this paper, RBFNN, FBNN and KNN classifier algorithms for face recognition using PCA and Rectangular feature have been implemented for the purpose of their comparison. The number of PCA features on different image size has been used along with four rectangular features. The recognition rate of different classifiers has been recorded in different conditions for the comparison. General Terms Rectangular Feature, Neural Network Classifiers, Face Recognition.

Recognition of Facial Expressions Based on Tracking and Selection of Discriminative Geometric Features

In this paper, we present a method for fully automatic facial expression recognition in facial image sequences using feature extracted from tracking of facial landmarks. The facial landmarks at the first frame of the image sequence under examination are initialized using elastic bunch graph matching (EBGM) algorithm and tracked in the consecutive video frame over time. At first, the most discriminative geometric features in terms of triangle are selected using multi-class AdaBoost with extreme learning machine (ELM) classifier. The features for facial expression recognition (FER) are extracted from AdaBoost selected most discriminative set of triangles composed of facial landmarks. Finally, the facial expressions are recognized using support vector machines (SVM) classification. The results on the extended Cohn-Kanade (CK+) and Multimedia Understanding Group (MUG) facial expression database shows a recognition accuracy of 97.80% and 95.50% respectively using proposed facial expression recognition system.

Elastic Bunch Graph Matching

Elastic Bunch Graph Matching

Biometric face recognition: from classical statistics to future challenges

Face recognition involves at least three major concepts from statistics: dimension reduction, feature extraction, and prediction. A selective review of algorithms, from seminal to state‐of‐the‐art, explores how these concepts persist as organizing principles in the field. Algorithms based directly upon classical statistical techniques include linear methods like principal component analysis and linear discriminant analysis. Nonlinear manifold methods, such as Laplacianfaces and Stiefel quotients, offer considerable performance improvements. Other noteworthy ideas include three‐dimensional morphable models, methods using local regions and/or alternative feature spaces (e.g., elastic bunch graph matching and local binary patterns) and sparse representation approaches. Opportunities for innovative statistical and collaborative research in face recognition are expanding in tandem with the growing complexity and diversity of applications.WIREs Comput Stat2013, 5:288–308. doi: 10.1002/wics.1262This article is categorized under:Applications of Computational Statistics > Defense and National SecuritySoftware for Computational Statistics > Artificial Intelligence and Expert SystemsApplications of Computational Statistics > Signal and Image Processing and Coding

Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.

48.3: Viewer's Eye Position Estimation using Single Camera

AbstractIn this paper, we propose a system that can estimate viewer's eye position using single camera in an autostereoscopic 3D display, which adopts Active Appearance Model (AAM), Elastic Bunch Graph Matching (EBGM) and Pose from Orthography and Scaling with ITerations (POSIT) algorithms for the eye position calculation. AAM is one of the most prominent facial feature detection algorithms for estimating viewer's eye position. However, this algorithm also has an issue in accuracy. EBGM is also known to be a very successful algorithm for the detection of viewer's facial feature points. Also, POSIT is known to calculate 3D pose from 2D image. In this paper, we propose a way to improve the accuracy of viewer's eye position by applying algorithms, AAM for initial estimation of facial features, EBGM for accurate facial feature position calculation and POSIT for calculation 3D facial position. The system estimates 3D facial position from extracted facial feature points to calculate accurate eye position. To calculate accurate eye position, we adopt POSIT algorithm when the viewer's face is rotated. It is verified through experiments that the newly proposed method performs better than existing method that uses single camera.

Face Recognition and Retrieval Using LLBP and URFB

Face recognition is one of the major issues in biometric technology. It identifies and/or verifies a person by using a 2D/3D physical characteristics of the face images. Several techniques have been proposed for solving a major problem in face recognition such as fisher face, elastic bunch graph matching and support vector machine. However there are still many challenge problems in face recognition system such as facial expressions, pose variations occlusion and illumination change. Those variations dramatically degrade the performance of face recognition system. It is essential to build an efficient system for face recognition. We introduce a novel face representation method for face recognition integrated with URFB called Local Line Binary Pattern (LLBP) summarizes the local spatial structure of an image by thresholding the local window with binary weight and introduce the decimal number as a texture presentation .Moreover it consumes less computational cost. The basic idea of LLBP is to first obtain the binary code both along the horizontal and vertical directions separately and its magnitude, which characterizes the change in image intensity such as edges and corners, is then computed along with the unified relevance feedback(URFB) shows the advantage over traditional retrieval mechanisms. To seamlessly combine texture feature based retrieval system, a query concept-dependent fusion strategy is automatically learned. Experimental results on ORL data base consisting of 400 images show that the proposed framework is widely scalable, and effective for recognition, classification and retrieval.

Orientation-based face recognition using multispectral imagery and score fusion

A new orientation-based face recognition method is proposed in this paper. The orientation analysis is performed with Gabor wavelet transform (GWT). The multispectral imagery includes the visible (RGB) and thermal (long-wave infrared) face images from the same group of subjects. The recognition performance of the new method is compared with that of three classical algorithms, principle component analysis, linear discriminant analysis, and elastic bunch graph matching. A score-level fusion of several algorithms versus multispectral images is explored and presented. Specifically, at each frequency band of GWT, an index number representing the strongest orientational response is selected, and then encoded in a binary number to favor the Hamming distance calculation. Multiple-band orientation codes are then organized into a face pattern byte (FPB) by using order statistics. With the FPB, Hamming distances are calculated and compared to achieve face identification. The FPB has the dimensionality of 8 bits per pixel and its performance will be compared to that of face pattern word (32 bits per pixel). The dimensionality of FPB can be further reduced down to 4 bits per pixel, called face pattern nibble. Experimental results with the multispectral faces of 96 subjects show that the proposed orientation-based face recognition method is very promising in contrast with three classical methods. Furthermore, the recognition performance with score-level fusion achieves 100% when tested on the entire multispectral database.

Evaluation of image resolution and super-resolution on face recognition performance

While researchers strive to improve automatic face recognition performance, the relationship between image resolution and face recognition performance has not received much attention. This relationship is examined systematically and a framework is developed such that results from super-resolution techniques can be compared. Three super-resolution techniques are compared with the Eigenface and Elastic Bunch Graph Matching face recognition engines. Parameter ranges over which these techniques provide better recognition performance than interpolated images is determined.