Efficient 3d Face Recognition Based On Pca

Author : Yagnesh Parmar
Publisher : GRIN Verlag
ISBN 13 : 3656302340
Total Pages : 8 pages
Book Rating : 4.46/5 ( download)

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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.

Author : Yinjie Lei
Publisher :
ISBN 13 :
Total Pages : 128 pages
Book Rating : 4.94/5 ( download)

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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...

Author : Rayner Alfred
Publisher : Springer
ISBN 13 : 9789811347979
Total Pages : 583 pages
Book Rating : 4.72/5 ( download)

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Download or read book Computational Science and Technology written by Rayner Alfred and published by Springer. This book was released on 2019-02-01 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features the proceedings of the Fifth International Conference on Computational Science and Technology 2018 (ICCST2018), held in Kota Kinabalu, Malaysia, on 29–30 August 2018. Of interest to practitioners and researchers, it presents exciting advances in computational techniques and solutions in this area. It also identifies emerging issues to help shape future research directions and enable industrial users to apply cutting-edge, large-scale and high-performance computational methods.

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.69/5 ( download)

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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.

Author : Yagnesh Parmar
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783848444014
Total Pages : 64 pages
Book Rating : 4.11/5 ( download)

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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.

Author : George Bebis
Publisher : Springer Nature
ISBN 13 : 3030337200
Total Pages : 718 pages
Book Rating : 4.09/5 ( download)

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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.

Author : Mohamed Daoudi
Publisher : John Wiley & Sons
ISBN 13 : 1118592638
Total Pages : 219 pages
Book Rating : 4.32/5 ( download)

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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

Author : Kresimir Delac
Publisher : BoD – Books on Demand
ISBN 13 : 9537619346
Total Pages : 250 pages
Book Rating : 4.43/5 ( download)

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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.

Author : Hassen Drira
Publisher :
ISBN 13 :
Total Pages : 135 pages
Book Rating : 4.82/5 ( download)

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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.

Author : Thirimachos Bourlai
Publisher : Springer
ISBN 13 : 3319285017
Total Pages : 388 pages
Book Rating : 4.16/5 ( download)

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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.