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 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 Enhancement and Extensions of Principal Component Analysis for Face Recognition written by and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Primarily due to increasing security demands and potential commercial and law enforcement applications, automatic face recognition has been a subject of extensive study in the past several decades, and remains an active field of research as of today. As a result, numerous techniques and algorithms for face recognition have been developed, many of them proving effective in one way or another. Nevertheless, it has been realized that constructing good solutions for automatic face recognition remains to be a challenge. The last two decades have witnessed significant progress in the development of new methods for automatic face recognition, some being effective and robust against pose, illumination and facial expression variations, while others being able to deal with large-scale data sets. On all accounts, the development of state-of-the-art face recognition systems has been recognized as one of the most successful applications of image analysis and understanding. Among others, the principal component analysis (PCA) developed in the early 1990s has been a popular unsupervised statistical method for data analysis, compression and visualization, and its application to face recognition problems has proven particularly successful. The importance of PCA consists in providing an efficient data compression with reduced information loss, and efficient implementation using singular value decomposition (SVD) of the data matrix. Since its original proposal, many variations of the standard PCA algorithm have emerged. This thesis is about enhancement and extensions of the standard PCA for face recognition. Our contributions are twofold. First, we develop a set of effective pre-processing techniques that can be employed prior to PCA in order to obtain improved recognition rate. Among these, a technique known as perfect histogram matching (PHM) is shown to perform very well. Other pre-processing methods we present in this thesis include an extended sparse PCA algorithm for dimensional.

Download or read book 3D Face Recognition Using PCA written by Yagnesh Parmar and published by LAP Lambert Academic Publishing. This book was released on 2012-04 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book 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 depth-values 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 images.

Download or read book Statistical Computing on Manifolds for 3D Face Analysis and Recognition written by Hassen Drira and published by . This book was released on 2011 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic face recognition has many benefits over other biometric technologies due to the natural, non-intrusive, and high throughput nature of face data acquisition. Thus, the techniques for face recognition have received a growing attention within the computer vision community over the past three decades. In terms of a modality for face imaging, a major advantage of 3D scans over 2D color imaging is that variations in illumination and scaling have less influence on the 3D scans.However, scan data often suffer from the problem of missing parts dueto self-occlusions or imperfections in scanning technologies. Additionally, variations in face data due to facial expressions are challenging to 3D face recognition. In order to be useful in real-world applications, 3D face recognition approaches should be able to successfully recognize face scans even in the presence of large expression-based deformations and missing data due to occlusions and pose variation. Most recent research has been directed towards expression-invariant techniques and spent less effort to handle the missing parts problem. Few approaches handles the missing part problem but none has performed on a full database containing real missing data, they simulate some missing parts. We present a common framework handling both large expressions and missing parts due to large pose variation. In addition, with the same framework, we are able to average surfaces and hierarchically organize databases to allow efficient searches. In presence of occlusion, we propose to delete and restore occluded parts. The surface is first represented by radial curves (emanating from the nose tip fo the 3D face). Then a base is built using PCA for each curve. Hence, the missing part of the curve can be restored by projecting the existing part of it on the base. PCA is applied on the tangent space of the mean curve as it is linear space. Once the occlusion was detected and removed, the occlusion challenge can be handled as a missing data problem. Hence, we apply the restoration framework and then apply our radial-curve-based 3D face recognition algorithm.

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 Pose Invariant Face Recognition Using Pca written by Patel Nehal and published by LAP Lambert Academic Publishing. This book was released on 2015-10-20 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both face detection and recognition are very curious areas in the field of image analysis, computer vision and pattern recognition that has received a big deal of attention over the last few years. It has been widely used for the purpose of security and forensic science for identify of an individual e.g. at the place of video surveillance, airports, traffic, terrorist attacks.To analyze the information of face images: faster, robust and efficient face detection and recognition algorithms are required. This system has been facing problems in recognizing subjects of varying poses, illumination conditions, facial expressions, and face occlusions. Due to variation in pose relative to camera certain features like smile, open eyes or mouth, left side or right side of mouth or eyes, occluded mouth or eyes can't be detected and extracted properly. It will be a critical task to detect a person with varying poses in vertical direction. In this work we present, face detection is performed by skin tone. Through PCA extract features and system is getting trained and tested. For face recognition process, Euclidean distance is measured and based on that minimum distance face is recognized

Download or read book Recent Advances in Face Recognition written by Kresimir Delac and published by BoD – Books on Demand. This book was released on 2008-12-01 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main idea and the driver of further research in the area of face recognition are security applications and human-computer interaction. Face recognition represents an intuitive and non-intrusive method of recognizing people and this is why it became one of three identification methods used in e-passports and a biometric of choice for many other security applications. This goal of this book is to provide the reader with the most up to date research performed in automatic face recognition. The chapters presented use innovative approaches to deal with a wide variety of unsolved issues.

Download or read book Advances in Visual Computing written by George Bebis and published by Springer Nature. This book was released on 2019-10-25 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th International Symposium on Visual Computing, ISVC 2019, held in Lake Tahoe, NV, USA in October 2019. The 100 papers presented in this double volume were carefully reviewed and selected from 163 submissions. The papers are organized into the following topical sections: Deep Learning I; Computer Graphics I; Segmentation/Recognition; Video Analysis and Event Recognition; Visualization; ST: Computational Vision, AI and Mathematical methods for Biomedical and Biological Image Analysis; Biometrics; Virtual Reality I; Applications I; ST: Vision for Remote Sensing and Infrastructure Inspection; Computer Graphics II; Applications II; Deep Learning II; Virtual Reality II; Object Recognition/Detection/Categorization; and Poster.

Download or read book New Approaches to Characterization and Recognition of Faces written by Peter Corcoran and published by BoD – Books on Demand. This book was released on 2011-08-01 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a baby, one of our earliest stimuli is that of human faces. We rapidly learn to identify, characterize and eventually distinguish those who are near and dear to us. We accept face recognition later as an everyday ability. We realize the complexity of the underlying problem only when we attempt to duplicate this skill in a computer vision system. This book is arranged around a number of clustered themes covering different aspects of face recognition. The first section presents an architecture for face recognition based on Hidden Markov Models; it is followed by an article on coding methods. The next section is devoted to 3D methods of face recognition and is followed by a section covering various aspects and techniques in video. Next short section is devoted to the characterization and detection of features in faces. Finally, you can find an article on the human perception of faces and how different neurological or psychological disorders can affect this.

Download or read book 3D Face Recognition System Based on 3D Eigenfaces written by Divyarajsinh Parmar and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: A face recognition system that solves the problem of changes in facial expression and mimics in 3D range images. So here, we propose a local variation detection and restoration method based eigenfaces using the principal component analysis (PCA). The depth map of a 3D facial image is first smoothed using median filter to minimize the local variation. The forefront nose point is selected to be the image center for alignment. The detected face shape is cropped & normalized to a standard image size of 101x101 pixels. Facial depth-valus are scaled between 0 and 255 for translation and scaling-invariant identification. The preprocessed face image is smoothed to minimize the local variations. The PCA 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 the person identity in the database of pre-recorded faces. The system performance is tested on the GavabDB databases. Experimental results show that the proposed method is able to identify subjects with different facial expression and mimics in the presence of noise in their 3D facial images.

Download or read book A Software Framework for PCA based Face Recognition written by Peng Peng and published by . This book was released on 2016 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplacable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component anaylsis (PCA) was proposed in the earliest stage; however, it is still attracting researchers in this field because of its property of reducing data dimensionality without losing important information. PCA-based face recognition has been studied for decades. There exist some image processing toolkits like OpenCV, which have implemented the PCA algorithm and associated methods. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle, which can hardly be solved by the toolkits. Furthermore, it still costs a lot of effort for software developers to integrate the implementations of the toolkits with their own applications. Therefore, the thesis provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Through various combination of these variations, at least 108 variations can be produced by the framework. Moreover, some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

Download or read book Towards the Development of an Efficient Integrated 3D Face Recognition System written by Xia Han and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this research was to enhance the methods towards the development of an efficient three dimensional face recognition system. More specifically, one of our aims was to investigate how the use of curvature of the diagonal profiles, extracted from 3D facial geometry models can help the neutral face recognition processes. Another aim was to use a gender classifier employed on 3D facial geometry in order to reduce the search space of the database on which facial recognition is performed. 3D facial geometry with facial expression possesses considerable challenges when it comes face recognition as identified by the communities involved in face recognition research. Thus, one aim of this study was to investigate the effects of the curvature-based method in face recognition under expression variations. Another aim was to develop techniques that can discriminate both expression-sensitive and expression-insensitive regions foriiface recognition based on non-neutral face geometry models. In the case of neutral face recognition, we developed a gender classification method using support vector machines based on the measurements of area and volume of selected regions of the face. This method reduced the search range of a database initially for a given image and hence reduces the computational time. Subsequently, in the characterisation of the face images, a minimum feature set of diagonal profiles, which we call T shape profiles, containing diacritic information were determined and extracted to characterise face models. We then used a method based on computing curvatures of selected facial regions to describe this feature set. In addition to the neutral face recognition, to solve the problem arising from data with facial expressions, initially, the curvature-based T shape profiles were employed and investigated for this purpose. For this purpose, the feature sets of the expression-invariant and expression-variant regions were determined respectively and described by geodesic distances and Euclidean distances. By using regression models the correlations between expressions and neutral feature sets were identified. This enabled us to discriminate expression-variant features and there was a gain in face recognition rate. The results of the study have indicated that our proposed curvature-based recognition, 3D gender classification of facial geometry and analysis of facial expressions, was capable of undertaking face recognition using a minimum set of features improving efficiency and computation.

Download or read book Face Recognition written by Miloš Oravec and published by BoD – Books on Demand. This book was released on 2010-04-01 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to bring together selected recent advances, applications and original results in the area of biometric face recognition. They can be useful for researchers, engineers, graduate and postgraduate students, experts in this area and hopefully also for people interested generally in computer science, security, machine learning and artificial intelligence. Various methods, approaches and algorithms for recognition of human faces are used by authors of the chapters of this book, e.g. PCA, LDA, artificial neural networks, wavelets, curvelets, kernel methods, Gabor filters, active appearance models, 2D and 3D representations, optical correlation, hidden Markov models and others. Also a broad range of problems is covered: feature extraction and dimensionality reduction (chapters 1-4), 2D face recognition from the point of view of full system proposal (chapters 5-10), illumination and pose problems (chapters 11-13), eye movement (chapter 14), 3D face recognition (chapters 15-19) and hardware issues (chapters 19-20).

Download or read book Face Recognition in Adverse Conditions written by De Marsico, Maria and published by IGI Global. This book was released on 2014-04-30 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Facial recognition software has improved by leaps and bounds over the past few decades, with error rates decreasing significantly within the past ten years. Though this is true, conditions such as poor lighting, obstructions, and profile-only angles have continued to persist in preventing wholly accurate readings. Face Recognition in Adverse Conditions examines how the field of facial recognition takes these adverse conditions into account when designing more effective applications by discussing facial recognition under real world PIE variations, current applications, and the future of the field of facial recognition research. The work is intended for academics, engineers, and researchers specializing in the field of facial recognition.

Download or read book Empirical Evaluation Methods in Computer Vision written by Henrik I. Christensen and published by World Scientific. This book was released on 2002 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the performance of medical image processing algorithms.

Download or read book Face Recognition Across the Imaging Spectrum written by Thirimachos Bourlai and published by Springer. This book was released on 2016-02-12 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This authoritative text/reference presents a comprehensive review of algorithms and techniques for face recognition (FR), with an emphasis on systems that can be reliably used in operational environments. Insights are provided by an international team of pre-eminent experts into the processing of multispectral and hyperspectral face images captured under uncontrolled environments. These discussions cover a variety of imaging sensors ranging from state-of-the-art visible and infrared imaging sensors, to RGB-D and mobile phone image sensors. A range of different biometric modalities are also examined, including face, periocular and iris. This timely volume is a mine of useful information for researchers, practitioners and students involved in image processing, computer vision, biometrics and security.