Download or read book 3D Facial Recognition Using Local Geometric Features for Pose and Expression Variation written by Fatimah Khalid and published by . This book was released on 2008 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning Based 3D Face Biometrics with Local Low level Geometrical Features written by Yinjie Lei and published by . This book was released on 2013 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: [Truncated abstract] Biometrics has been an active research area due to its enormous potential applications in video surveillance, human-machine interaction and access control systems. Among the biometric traits, the human face is the most publicly accepted biometric because of its non-intrusiveness and easy data acquisition. Most of the work on face recognition has been accomplished using 2D data. 2D face recognition systems are not robust to variations in pose, illumination conditions and facial expressions. With the rapid advancements in the development of data capturing technologies (e.g. Minolta Vivid and Microsoft Kinect), the acquisition of 3D data is becoming a more feasible task. 3D data processing has the potential to overcome the limitations and drawbacks faced by 2D facial data. Most of the existing 3D face recognition systems rely on the surface registration of the gallery and probe faces and/or on complex feature matching techniques. These methods are sensitive to facial expression and computationally expensive and are not suitable for real-world applications. In this thesis, we present novel algorithms based on low-level geometrical signatures which can be extracted at a low computational cost. To address the issue of facial expression variations, we adopt various machine learning techniques. This thesis is organized as a set of papers published in journals or currently under review. Three different local geometric feature based approaches have been proposed and their efficiency has been demonstrated through extensive experimental evaluations on the largest publicly available 3D face datasets. First, a fast and fully automatic approach based on four kinds of low-level geometrical features collected from the semi-rigid facial regions was devised and used to represent 3D faces. As a result, the effects of the deformed facial regions are avoided. The extracted features revealed to be efficient in computation and robust in the presence of facial expressions. A region-based histogram descriptor computed from these features was used as a single feature vector for a 3D face. The resulting feature vectors are independent of the coordinate system and hence can be tolerant to minor pose variations. A Support Vector Machine (SVM) was then trained as a classifier based on the proposed histogram descriptors to recognize any test face. In order to combine the contributions of the two semi-rigid facial regions (eyesforehead and nose), both feature-level and score-level fusion schemes are tested and compared. The experimental results demonstrate that feature-level fusion achieves a higher performance compared to score-level fusion. Second, in order to further increase the computational efficiency and robustness, a computationally efficient 3D face recognition approach is presented based on a novel facial signature called Angular Radial Signature (ARS). This approach extracts a set of ARS features from the semi-rigid regions of a 3D face. It was demonstrated that the extraction of these signatures is highly efficient (low computational cost). The Kernel Principal Component Analysis (KPCA) is subsequently used to extract the mid-level features from the ARSs to achieve a greater discriminative power and to deal with the linearly inseparable classification problem...

Download or read book Engineered and Learned Features for Face and Facial Expression Recognition written by Said Moh'd Said Elaiwat and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Face and facial expression recognition play a crucial role in many applications such as biometrics, human computer interactions and non-verbal communications. The human face can provide important clues/cues to identify people, and determine their emotional state, even without their explicit cooperation. However, variations in illumination conditions, facial pose, occlusion and facial expression (for face recognition), can dramatically degrade the performance of face and facial expression recognition systems. To address these challenges, this thesis presents novel feature extraction methods based on hand-engineered global and local features geared towards the problem of face recognition in still images. Novel feature learning methods are also proposed for the task of video based face and facial expression recognition. The proposed methods are capable of providing robust and distinctive facial features in the presence of variations in illumination, occlusion, pose and image resolution. The thesis starts by investigating the ability of Curvelet transform to extract robust global features for the task of 3D face recognition under different facial expressions. The benefits of fusing 3D and 2D Curvelet features is also investigated to achieve multimodal face identification. While such an approach proposed above extracts robust features from semi-rigid regions, it is often hard to automatically detect such regions across different datasets. Thus, a novel Curvelet local feature approach is proposed to extract local features rather than global features. The proposed approach relies on a novel multimodal keypoint detector capable of repeatably identifying keypoints on textured 3D face surfaces. Unique local surface descriptors are then constructed around each detected keypoint by integrating curvelet elements of different orientations. Unlike previously reported curvelet-based face recognition algorithms, which extract global features from textured faces only, our algorithm extracts both texture and 3D local features. The thesis also addresses the problem of face recognition from low resolution videos (e.g, security camera). This problem introduces new challenges requiring a method capable of exploiting the temporal information or/and appearance variations within image sequences (videos) during the feature extraction.To address these issues, a novel feature learning RBM-based model is proposed to automatically extract the best features, which can represent the semantic knowledge within videos (image sets). The structure of the proposed model involves two hidden sets used to encode the dominant appearances (facial features) and temporal information within videos (image sets). To learn the proposed model, an extension of the standard Constructive Divergence algorithm is proposed to facilitate the encoding of two different feature types (i.e.,facial features and temporal information). For video based facial expression recognition, the thesis also proposes a novel feature learning RBM-based model to learn effectively the relationships (or transformations) between image pairs associated with different facial expressions. The proposed model has the ability to disentangle these transformations (e.g. pose variations and facial expressions) by encoding them into two different hidden sets. The first hidden set is used to encode facial-expression morphlets, while the second hidden set is used to encode non-facial-expression morphlets. This is achieved using an algorithm, dubbed Quadripartite Contrastive Divergence.

Download or read book Efficient 3D face recognition based on PCA written by Yagnesh Parmar and published by GRIN Verlag. This book was released on 2012-11-05 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Project Report from the year 2012 in the subject Engineering - Computer Engineering, Gujarat University, course: Electronics and communication, language: English, abstract: This thesis describes a face recognition system that overcomes the problem of changes in gesture and mimics in three-dimensional (3D) range images. Here, we propose a local variation detection and restoration method based on the two-dimensional (2D) principal component analysis (PCA). The depth map of a 3D facial image is first smoothed using median filter to minimize the local variation. The detected face shape is cropped & normalized to a standard image size of 101x101 pixels and the forefront nose point is selected to be the image center. Facial depthvalues are scaled between 0 and 255 for translation and scaling-invariant identification. The preprocessed face image is smoothed to minimize the local variations. The 2DPCA is applied to the resultant range data and the corresponding principal-(or eigen-) images are used as the characteristic feature vectors of the subject to find his/her identity in the database of pre-recorded faces. The system's performance is tested against the GavabDB facial databases. Experimental results show that the proposed method is able to identify subjects with different gesture and mimics in the presence of noise in their 3D facial image.

Download or read book Face Detection and Modeling for Recognition written by Rein-Lien Hsu and published by . This book was released on 2002 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face recognition has received substantial attention from researchers in biometrics, computer vision, pattern recognition, and cognitive psychology communities because of the increased attention being devoted to security, man-machine communication, content-based image retrieval, and image/video coding. We have proposed two automated recognition paradigms to advance face recognition technology. Three major tasks involved in face recognition systems are: (i) face detection, (ii) face modeling, and (iii) face matching. We have developed a face detection algorithm for color images in the presence of various lighting conditions as well as complex backgrounds. Our detection method first corrects the color bias by a lighting compensation technique that automatically estimates the parameters of reference white for color correction. We overcame the difficulty of detecting the low-luma and high-luma skin tones by applying a nonlinear transformation to the Y CbCr color space. Our method generates face candidates based on the spatial arrangement of detected skin patches. We constructed eye, mouth, and face boundary maps to verify each face candidate. Experimental results demonstrate successful detection of faces with different sizes, color, position, scale, orientation, 3D pose, and expression in several photo collections. 3D human face models augment the appearance-based face recognition approaches to assist face recognition under the illumination and head pose variations. For the two proposed recognition paradigms, we have designed two methods for modeling human faces based on (i) a generic 3D face model and an individual's facial measurements of shape and texture captured in the frontal view, and (ii) alignment of a semantic face graph, derived from a generic 3D face model, onto a frontal face image.

Download or read book Low level Fusion and Deformation Modeling for Textured 3D Face Biometrics written by Faisal Radhi M. Al-Osaimi and published by . This book was released on 2009 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: [Truncated abstract] Automatic face recognition has many crucial applications in a range of domains including human-machine interaction and security. The non-intrusive nature of face recognition is a key reason behind its suitability to such a broad range of applications. However, often these applications require high recognition accuracies and robustness which are challenging to achieve due to variations. Variations in pose, facial expression and illumination conditions may obscure the essential visual and/or geometric cues for recognition. The ability of a face recognition system to represent and match faces such that the utilized descriptive cues outweigh the associated variations is a key factor in enhancing its accuracy and robustness. This thesis presents novel algorithms that strive to achieve this goal by combining the descriptive cues in textured 3D facial scans at lower levels (data and feature fusion levels) and/or modeling the facial expression and illumination variations. An approach for combining fields of weak local and global geometrical cues (computed for each scan vertex) at the feature level was devised and used for compact representation and matching of 3D faces. The extracted fields are independent of the coordinate system in which the scan vertices were defined and hence the representation is tolerant to minor pose variations. The combined representation has demonstrated considerably high descriptiveness. In another presented approach, only local regions around key-points on the facial scans were considered. Heuristic measures for the descriptiveness of 3D local regions were defined and used for robust matching. The matching enforces consistent 3D rigid transformations and global structures among matched scans but on the other hand it allows for few mismatches among the local regions. Hence, it avoids the effects of deformed regions on matching. While the above strategies of combining information with and without the avoidance of the deformed regions gave a high recognition performance for scans under neutral expressions (minor deformations), the reported variation modeling approaches have shown higher recognition accuracies for faces with neutral or nonneutral expressions. The thesis also demonstrates that low level multimodal fusion in conjunction with variation modeling gives a higher performance than unimodal recognition and higher level fusion. Another contribution of this thesis is a novel approach for learning and morphing 3D expression deformations of 3D facial scans to those of target scans. This neutralizes the effects of expression variations among matched scans. In an extension of this approach, unseen facial scans under any facial expression were decomposed into estimates of neutral 3D faces and deformation residues. This is a very challenging problem due to the lack of the target scan. The estimated neutral faces and the deformations residues were used for face recognition and expression classification, respectively. The thesis also presents a novel approach for illumination normalization of the facial texture in textured scans...

Download or read book Towards Three dimensional Face Recognition in the Real written by Huibin Li and published by . This book was released on 2013 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the natural, non-intrusive, easily collectible, widespread applicability, machine-based face recognition has received significant attention from the biometrics community over the past three decades. Compared with traditional appearance-based (2D) face recognition, shape-based (3D) face recognition is more stable to illumination variations, small head pose changes, and varying facial cosmetics. However, 3D face scans captured in unconstrained conditions may lead to various difficulties, such as non-rigid deformations caused by varying expressions, data missing due to self occlusions and external occlusions, as well as low-quality data as a result of some imperfections in the scanning technology. In order to deal with those difficulties and to be useful in real-world applications, in this thesis, we propose two 3D face recognition approaches: one is focusing on handling various expression changes, while the other one can recognize people in the presence of large facial expressions, occlusions and large pose various. In addition, we provide a provable and practical surface meshing algorithm for data-quality improvement. To deal with expression issue, we assume that different local facial region (e.g. nose, eyes) has different intra-expression/inter-expression shape variability, and thus has different importance. Based on this assumption, we design a learning strategy to find out the quantification importance of local facial regions in terms of their discriminating power. For facial description, we propose a novel shape descriptor by encoding the micro-structure of multi-channel facial normal information in multiple scales, namely, Multi-Scale and Multi-Component Local Normal Patterns (MSMC-LNP). It can comprehensively describe the local shape changes of 3D facial surfaces by a set of LNP histograms including both global and local cues. For face matching, Weighted Sparse Representation-based Classifier (W-SRC) is formulated based on the learned quantification importance and the LNP histograms. The proposed approach is evaluated on four databases: the FRGC v2.0, Bosphorus, BU-3DFE and 3D-TEC, including face scans in the presence of diverse expressions and action units, or several prototypical expressions with different intensities, or facial expression variations combine with strong facial similarities (i.e. identical twins). Extensive experimental results show that the proposed 3D face recognition approach with the use of discriminative facial descriptors can be able to deal with expression variations and perform quite accurately over all databases, and thereby has a good generalization ability. To deal with expression and data missing issues in an uniform framework, we propose a mesh-based registration free 3D face recognition approach based on a novel local facial shape descriptor and a multi-task sparse representation-based face matching process. [...].

Download or read book 3D Shape Analysis written by Hamid Laga and published by John Wiley & Sons. This book was released on 2019-01-07 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: An in-depth description of the state-of-the-art of 3D shape analysis techniques and their applications This book discusses the different topics that come under the title of "3D shape analysis". It covers the theoretical foundations and the major solutions that have been presented in the literature. It also establishes links between solutions proposed by different communities that studied 3D shape, such as mathematics and statistics, medical imaging, computer vision, and computer graphics. The first part of 3D Shape Analysis: Fundamentals, Theory, and Applications provides a review of the background concepts such as methods for the acquisition and representation of 3D geometries, and the fundamentals of geometry and topology. It specifically covers stereo matching, structured light, and intrinsic vs. extrinsic properties of shape. Parts 2 and 3 present a range of mathematical and algorithmic tools (which are used for e.g., global descriptors, keypoint detectors, local feature descriptors, and algorithms) that are commonly used for the detection, registration, recognition, classification, and retrieval of 3D objects. Both also place strong emphasis on recent techniques motivated by the spread of commodity devices for 3D acquisition. Part 4 demonstrates the use of these techniques in a selection of 3D shape analysis applications. It covers 3D face recognition, object recognition in 3D scenes, and 3D shape retrieval. It also discusses examples of semantic applications and cross domain 3D retrieval, i.e. how to retrieve 3D models using various types of modalities, e.g. sketches and/or images. The book concludes with a summary of the main ideas and discussions of the future trends. 3D Shape Analysis: Fundamentals, Theory, and Applications is an excellent reference for graduate students, researchers, and professionals in different fields of mathematics, computer science, and engineering. It is also ideal for courses in computer vision and computer graphics, as well as for those seeking 3D industrial/commercial solutions.

Download or read book 3D Face Modeling Analysis and Recognition written by Mohamed Daoudi and published by John Wiley & Sons. This book was released on 2013-06-11 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: 3D Face Modeling, Analysis and Recognition presents methodologies for analyzing shapes of facial surfaces, develops computational tools for analyzing 3D face data, and illustrates them using state-of-the-art applications. The methodologies chosen are based on efficient representations, metrics, comparisons, and classifications of features that are especially relevant in the context of 3D measurements of human faces. These frameworks have a long-term utility in face analysis, taking into account the anticipated improvements in data collection, data storage, processing speeds, and application scenarios expected as the discipline develops further. The book covers face acquisition through 3D scanners and 3D face pre-processing, before examining the three main approaches for 3D facial surface analysis and recognition: facial curves; facial surface features; and 3D morphable models. Whilst the focus of these chapters is fundamentals and methodologies, the algorithms provided are tested on facial biometric data, thereby continually showing how the methods can be applied. Key features: • Explores the underlying mathematics and will apply these mathematical techniques to 3D face analysis and recognition • Provides coverage of a wide range of applications including biometrics, forensic applications, facial expression analysis, and model fitting to 2D images • Contains numerous exercises and algorithms throughout the book

Download or read book Robust Face Recognition Based on Three Dimensional Data written by Di Huang and published by . This book was released on 2011 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: The face is one of the best biometrics for person identification and verification related applications, because it is natural, non-intrusive, and socially weIl accepted. Unfortunately, an human faces are similar to each other and hence offer low distinctiveness as compared with other biometrics, e.g., fingerprints and irises. Furthermore, when employing facial texture images, intra-class variations due to factors as diverse as illumination and pose changes are usually greater than inter-class ones, making 2D face recognition far from reliable in the real condition. Recently, 3D face data have been extensively investigated by the research community to deal with the unsolved issues in 2D face recognition, Le., illumination and pose changes. This Ph.D thesis is dedicated to robust face recognition based on three dimensional data, including only 3D shape based face recognition, textured 3D face recognition as well as asymmetric 3D-2D face recognition. In only 3D shape-based face recognition, since 3D face data, such as facial pointclouds and facial scans, are theoretically insensitive to lighting variations and generally allow easy pose correction using an ICP-based registration step, the key problem mainly lies in how to represent 3D facial surfaces accurately and achieve matching that is robust to facial expression changes. In this thesis, we design an effective and efficient approach in only 3D shape based face recognition. For facial description, we propose a novel geometric representation based on extended Local Binary Pattern (eLBP) depth maps, and it can comprehensively describe local geometry changes of 3D facial surfaces; while a 81FT -based local matching process further improved by facial component and configuration constraints is proposed to associate keypoints between corresponding facial representations of different facial scans belonging to the same subject. Evaluated on the FRGC v2.0 and Gavab databases, the proposed approach proves its effectiveness. Furthermore, due tq the use of local matching, it does not require registration for nearly frontal facial scans and only needs a coarse alignment for the ones with severe pose variations, in contrast to most of the related tasks that are based on a time-consuming fine registration step. Considering that most of the current 3D imaging systems deliver 3D face models along with their aligned texture counterpart, a major trend in the literature is to adopt both the 3D shape and 2D texture based modalities, arguing that the joint use of both clues can generally provides more accurate and robust performance than utilizing only either of the single modality. Two important factors in this issue are facial representation on both types of data as well as result fusion. In this thesis, we propose a biological vision-based facial representation, named Oriented Gradient Maps (OGMs), which can be applied to both facial range and texture images. The OGMs simulate the response of complex neurons to gradient information within a given neighborhood and have properties of being highly distinctive and robust to affine illumination and geometric transformations. The previously proposed matching process is then adopted to calculate similarity measurements between probe and gallery faces. Because the biological vision-based facial representation produces an OGM for each quantized orientation of facial range and texture images, we finally use a score level fusion strategy that optimizes weights by a genetic algorithm in a learning pro cess. The experimental results achieved on the FRGC v2.0 and 3DTEC datasets display the effectiveness of the proposed biological vision-based facial description and the optimized weighted sum fusion. [...].

Download or read book Applied Algorithms written by Prosenjit Gupta and published by Springer. This book was released on 2014-01-08 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Conference on Applied Algorithms, ICAA 2014, held in Kolkata, India, in January 2014. ICAA is a new conference series with a mission to provide a quality forum for researchers working in applied algorithms. Papers presenting original contributions related to the design, analysis, implementation and experimental evaluation of efficient algorithms and data structures for problems with relevant real-world applications were sought, ideally bridging the gap between academia and industry. The 21 revised full papers presented together with 7 short papers were carefully reviewed and selected from 122 submissions.

Download or read book 3D Face Analysis written by Zhao, Xi and published by . This book was released on 2010 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Ph.D thesis work is dedicated to automatic facial analysis in 3D, including facial landmarking and facial expression recognition. Indeed, facial expression plays an important role both in verbal and non verbal communication, and in expressing emotions. Thus, automatic facial expression recognition has various purposes and applications and particularly is at the heart of "intelligent" human-centered human/computer(robot) interfaces. Meanwhile, automatic landmarking provides aprior knowledge on location of face landmarks, which is required by many face analysis methods such as face segmentation and feature extraction used for instance for expression recognition. The purpose of this thesis is thus to elaborate 3D landmarking and facial expression recognition approaches for finally proposing an automatic facial activity (facial expression and action unit) recognition solution.In this work, we have proposed a Bayesian Belief Network (BBN) for recognizing facial activities, such as facial expressions and facial action units. A StatisticalFacial feAture Model (SFAM) has also been designed to first automatically locateface landmarks so that a fully automatic facial expression recognition system can be formed by combining the SFAM and the BBN. The key contributions are the followings. First, we have proposed to build a morphable partial face model, named SFAM, based on Principle Component Analysis. This model allows to learn boththe global variations in face landmark configuration and the local ones in terms of texture and local geometry around each landmark. Various partial face instances can be generated from SFAM by varying model parameters. Secondly, we have developed a landmarking algorithm based on the minimization an objective function describing the correlation between model instances and query faces. Thirdly, we have designed a Bayesian Belief Network with a structure describing the casual relationships among subjects, expressions and facial features. Facial expression oraction units are modelled as the states of the expression node and are recognized by identifying the maximum of beliefs of all states. We have also proposed a novel method for BBN parameter inference using a statistical feature model that can beconsidered as an extension of SFAM. Finally, in order to enrich information usedfor 3D face analysis, and particularly 3D facial expression recognition, we have also elaborated a 3D face feature, named SGAND, to characterize the geometry property of a point on 3D face mesh using its surrounding points.The effectiveness of all these methods has been evaluated on FRGC, BU3DFEand Bosphorus datasets for facial landmarking as well as BU3DFE and Bosphorus datasets for facial activity (expression and action unit) recognition.

Download or read book Novel Algorithms for 3D Human Face Recognition written by Shalini Gupta and published by . This book was released on 2008 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated human face recognition is a computer vision problem of considerable practical significance. Existing two dimensional (2D) face recognition techniques perform poorly for faces with uncontrolled poses, lighting and facial expressions. Face recognition technology based on three dimensional (3D) facial models is now emerging. Geometric facial models can be easily corrected for pose variations. They are illumination invariant, and provide structural information about the facial surface. Algorithms for 3D face recognition exist, however the area is far from being a matured technology. In this dissertation we address a number of open questions in the area of 3D human face recognition. Firstly, we make available to qualified researchers in the field, at no cost, a large Texas 3D Face Recognition Database, which was acquired as a part of this research work. This database contains 1149 2D and 3D images of 118 subjects. We also provide 25 manually located facial fiducial points on each face in this database. Our next contribution is the development of a completely automatic novel 3D face recognition algorithm, which employs discriminatory anthropometric distances between carefully selected local facial features. This algorithm neither uses general purpose pattern recognition approaches, nor does it directly extend 2D face recognition techniques to the 3D domain. Instead, it is based on an understanding of the structurally diverse characteristics of human faces, which we isolate from the scientific discipline of facial anthropometry. We demonstrate the effectiveness and superior performance of the proposed algorithm, relative to existing benchmark 3D face recognition algorithms. A related contribution is the development of highly accurate and reliable 2D+3D algorithms for automatically detecting 10 anthropometric facial fiducial points. While developing these algorithms, we identify unique structural/textural properties associated with the facial fiducial points. Furthermore, unlike previous algorithms for detecting facial fiducial points, we systematically evaluate our algorithms against manually located facial fiducial points on a large database of images. Our third contribution is the development of an effective algorithm for computing the structural dissimilarity of 3D facial surfaces, which uses a recently developed image similarity index called the complex-wavelet structural similarity index. This algorithm is unique in that unlike existing approaches, it does not require that the facial surfaces be finely registered before they are compared. Furthermore, it is nearly an order of magnitude more accurate than existing facial surface matching based approaches. Finally, we propose a simple method to combine the two new 3D face recognition algorithms that we developed, resulting in a 3D face recognition algorithm that is competitive with the existing state-of-the-art algorithms.

Download or read book Facial Multi characteristics And Applications written by Bob Zhang and published by World Scientific. This book was released on 2018-11-19 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: What features or information can we observe from a face, and how can these information help us to understand the person concerned, in terms of their well-being and what can we learn about and from each given feature? This book answers these questions by first dividing a face's multiple characteristics into two main categories: original (or physiological) features and features that change over a lifetime. The first category, original features, may be further divided into two sub-classes: features special (or unique) to an individual, and features common to a particular group. The second, changed features, can also be subdivided into two groups: features altered due to disease or features altered by other external factors. From these four sub-categories, four different applications — facial identification using original and special features; beauty analysis using original common features; facial diagnosis by disease changed features; and expression recognition through affect-changed features — are identified.The book will benefit researchers, professionals, and graduate students working in the field of computer vision, pattern recognition, security/clinical practice, and beauty analysis, and will also be useful for interdisciplinary research.

Download or read book Geometric Expression Invariant 3D Face Recognition Using Statistical Iscriminant Models written by and published by . This book was released on 2009 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to achieve expression-invariant face recognition systems, we have employed a tensor algebra framework to represent 3D face data with facial expressions in a parsimonious space. Face variation factors are organized in particular subject and facial expression modes. We manipulate this using single value decomposition on sub-tensors representing one variation mode. This framework possesses the ability to deal with the shortcomings of PCA in less constrained environments and still preserves the integrity of the 3D data. The results show improved recognition rates for faces and facial expressions, even recognizing high intensity expressions that are not in the training datasets.

Download or read book Unconstrained Face Recognition written by Shaohua Kevin Zhou and published by Springer Science & Business Media. This book was released on 2006-10-11 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face recognition has been actively studied over the past decade and continues to be a big research challenge. Just recently, researchers have begun to investigate face recognition under unconstrained conditions. Unconstrained Face Recognition provides a comprehensive review of this biometric, especially face recognition from video, assembling a collection of novel approaches that are able to recognize human faces under various unconstrained situations. The underlying basis of these approaches is that, unlike conventional face recognition algorithms, they exploit the inherent characteristics of the unconstrained situation and thus improve the recognition performance when compared with conventional algorithms. Unconstrained Face Recognition is structured to meet the needs of a professional audience of researchers and practitioners in industry. This volume is also suitable for advanced-level students in computer science.

Download or read book Social Signal Processing written by Judee K. Burgoon and published by Cambridge University Press. This book was released on 2017-05-08 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social Signal Processing is the first book to cover all aspects of the modeling, automated detection, analysis, and synthesis of nonverbal behavior in human-human and human-machine interactions. Authoritative surveys address conceptual foundations, machine analysis and synthesis of social signal processing, and applications. Foundational topics include affect perception and interpersonal coordination in communication; later chapters cover technologies for automatic detection and understanding such as computational paralinguistics and facial expression analysis and for the generation of artificial social signals such as social robots and artificial agents. The final section covers a broad spectrum of applications based on social signal processing in healthcare, deception detection, and digital cities, including detection of developmental diseases and analysis of small groups. Each chapter offers a basic introduction to its topic, accessible to students and other newcomers, and then outlines challenges and future perspectives for the benefit of experienced researchers and practitioners in the field.