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face-recognition
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Head pose estimation and face recognition using a non-linear tensor-based model
, Article IET Computer Vision ; Volume 8, Issue 1 , Pages 54-65 , 2014 , p. 54-65 ; ISSN: 17519632 ; Kasaei, S ; Sharif University of Technology
Abstract
Although the ability to estimate the face pose and recognise its identity are common human abilities, they are still a challenge in computer vision context. In this study, the authors aim to overcome these difficulties by learning a non-linear tensorbased model based on multi-linear decomposition. Proposed model maps the high-dimensional image space into low-dimensional pose manifold. For preserving the actual distance along the manifold shape, a graph-based distance measure is proposed. Also, to compensate for the limited number of training poses, mirrored images are added to training ones to improve the recognition accuracy. For performance evaluation of the proposed method, experiments...
Multiview face recognition based on multilinear decomposition and pose manifold
, Article IET Image Processing ; Volume 8, Issue 5 , 2014 , Pages 300-309 ; ISSN: 17519659 ; Kasaei, S ; Sharif University of Technology
Abstract
One major challenge encountered in face recognition is how to handle the wide pose variation and in-depth rotations of head. A multiview face recognition method is proposed in this study that addresses this challenge based on multilinear decomposition approach and pose subspace. In order to preserve the pose manifold geometry among different individuals in pose subspace, a pose-biased distance measure is proposed. In addition, as one of the impediments in manifold-based methods is the lack of sufficient data, a new half-ellipsoid-based pose generation method is presented. For performance evaluation of the proposed multiview face recognition method, three different experiments are run on...
Face recognition across large pose variations via boosted tied factor analysis
, Article 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011, 5 January 2011 through 7 January 2011 ; January , 2011 , Pages 190-195 ; 9781424494965 (ISBN) ; Rabiee, H. R ; Rohban, M. H ; Sharif University of Technology
2011
Abstract
In this paper, we propose an ensemble-based approach to boost performance of Tied Factor Analysis(TFA) to overcome some of the challenges in face recognition across large pose variations. We use Adaboost.m1 to boost TFA which has shown to possess state-of-the-art face recognition performance under large pose variations. To this end, we have employed boosting as a discriminative training in the TFA as a generative model. In this model, TFA is used as a base classiœr for the boosting algorithm and a weighted likelihood model for TFA is proposed to adjust the importance of each training data. Moreover, a modiÔd weighting and a diversity criterion are used to generate more diverse classiœrs in...
Two-dimensional heteroscedastic feature extraction technique for face recognition
, Article Computing and Informatics ; Volume 30, Issue 5 , 2011 , Pages 965-986 ; 13359150 (ISSN) ; Manzuri Shalmani, M. T ; Sharif University of Technology
2011
Abstract
One limitation of vector-based LDA and its matrix-based extension is that they cannot deal with heteroscedastic data. In this paper, we present a novel two-dimensional feature extraction technique for face recognition which is capable of handling the heteroscedastic data in the dataset. The technique is a general form of two-dimensional linear discriminant analysis. It generalizes the interclass scatter matrix of two-dimensional LDA by applying the Chernoff distance as a measure of separation of every pair of clusters with the same index in different classes. By employing the new distance, our method can capture the discriminatory information presented in the difference of covariance...
Comparing performance of metaheuristic algorithms for finding the optimum structure of CNN for face recognition
, Article International Journal of Nonlinear Analysis and Applications ; Volume 11, Issue 1 , 2020 , Pages 301-319 ; Pooyan, M ; Manzuri, M. T ; Sharif University of Technology
Semnan University, Center of Excellence in Nonlinear Analysis and Applications
2020
Abstract
Local and global based methods are two main trends for face recognition. Local approaches extract salient features by processing different parts of the image whereas global approaches find a general template for face of each person. Unfortunately, most global approaches work under controlled envi-ronments and they are sensitive to changes in the illumination. On the other hand, local approaches are more robust but finding their optimal parameters is a challenging task. This work proposes a new local-based approach that automatically tunes its parameters. The proposed method incorporates different techniques. In the first step, convolutional neural network (CNN) is employed as a trainable...
Face recognition using boosted regularized linear discriminant analysis
, Article ICCMS 2010 - 2010 International Conference on Computer Modeling and Simulation, 22 January 2010 through 24 January 2010, Sanya ; Volume 2 , 2010 , Pages 89-93 ; 9780769539416 (ISBN) ; Kasaei, S ; Alizadeh, S ; Sharif University of Technology
2010
Abstract
Boosting is a general method for improving the accuracy of any given learning algorithm. In this paper, we have proposed the boosting method for face recognition (FR) that improves the linear discriminant analysis (LDA)-based technique. The improvement is achieved by incorporating the regularized LDA (R-LDA) technique into the boosting framework. R-LDA is based on a new regularized Fisher's discriminant criterion, which is particularly robust against the small sample size problem compared to the traditional one used in LDA. The AdaBoost technique is utilized within this framework to generalize a set of simple FR subproblems and their corresponding LDA solutions and combines the results from...
A robust sparse representation based face recognition system for smartphones
, Article 2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings, 12 December 2015 ; 2015 ; 9781509013500 (ISBN) ; Joneidi, M ; Rezaeifar, S ; Baradaran Shokouhi, S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2015
Abstract
Many research works have been done in face recognition during the last years that indicates the importance of face recognition systems in many applications including identity authentication. In this paper we propose an approach for face recognition which is suitable for unconstrained image acquisition and has a low computational cost. Since in practical applications such as in smartphones, imaging conditions are not limited to existing images in the database, robustness of the recognition algorithm is very important. Here a sparse representation framework is proposed which achieves some degree of robustness. Using double sparse representation the high computational cost of sparsity-based...
Genetic algorithm-optimised structure of convolutional neural network for face recognition applications
, Article IET Computer Vision ; Volume 10, Issue 6 , 2016 , Pages 559-566 ; 17519632 (ISSN) ; Pooyan, M ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institution of Engineering and Technology
2016
Abstract
Proposing a proper method for face recognition is still a challenging subject in biometric and computer vision applications. Although some reliable systems were introduced under relatively controlled conditions, their recognition rate is not satisfactory in the general settings. This is especially true when there are variations in pose, illumination, and facial expression. To alleviate these problems, a hybrid face recognition system is proposed which benefits from the superiority of both convolutional neural network (CNN) and support vector machine (SVM). To this end, first a genetic algorithm is employed to find the optimum structure of CNN. Then, the performance of the system is improved...
Designing and Implementing a Multi-View Face Recognition System
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emadoddin (Supervisor) ; Marvasti, Farrokh (Supervisor)
Abstract
This thesis presents a novel approach for detection and recognition of multi-view faces whose location is unknown and the illumination conditions are varying. The illumination is a big problem in the face detection and recognition aspects. Two completely different methods are proposed for face detection in this thesis. Our proposed methods do not use the skin colour information for face detection. The detection of faces is accomplished after cancelling the effect of the various illumination conditions. Because of the independency of the approaches to the face’s skin colour, persons with every kind of skin colours are detected even in completely dark environments. Next, the detected faces are...
Heteroscedastic multilinear discriminant analysis for face recognition
, Article Proceedings - International Conference on Pattern Recognition, 23 August 2010 through 26 August 2010, Istanbul ; 2010 , Pages 4287-4290 ; 10514651 (ISSN) ; 9780769541099 (ISBN) ; Manzuri Shalmani, M. T ; Sharif University of Technology
2010
Abstract
There is a growing attention in subspace learning using tensor-based approaches in high dimensional spaces. In this paper we first indicate that these methods suffer from the Heteroscedastic problem and then propose a new approach called Heteroscedastic Multilinear Discriminant Analysis (HMDA). Our method can solve this problem by utilizing the pairwise chernoff distance between every pair of clusters with the same index in different classes. We also show that our method is a general form of Multilinear Discriminant Analysis (MDA) approach. Experimental results on CMU-PIE, AR and AT&T face databases demonstrate that the proposed method always perform better than MDA in term of classification...
A survey on deep learning based approaches for action and gesture recognition in image sequences
, Article 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, 30 May 2017 through 3 June 2017 ; 2017 , Pages 476-483 ; 9781509040230 (ISBN) ; Clapes, A ; Bellantonio, M ; Escalante, H. J ; Ponce Lopez, V ; Baro, X ; Guyon, I ; Kasaei, S ; Escalera, S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2017
Abstract
The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions. We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research. © 2017 IEEE
Face recognition under varying illumination based on histogram equalized processed images
, Article ISPA 2011 - 7th International Symposium on Image and Signal Processing and Analysis ; 2011 , Pages 349-354 ; 9789531841597 (ISBN) ; Jamzad, M ; European Association for Signal Processing (EURASIP); IEEE Signal Processing Society; IEEE Region 8; IEEE Croatia Section; IEEE Croatia Section Signal Processing Chapter ; Sharif University of Technology
Abstract
This paper proposes an enhanced illumination compensation algorithm, which can compensate for the uneven illuminations on human faces and reconstruct face images in normal lighting conditions. To detect the illumination category, based on 65 categories in YaleB database, the images processed using Block-based Histogram Equalization (BHE) is compared with the original face image processed using histogram equalization (HE). Based on the identified illumination category, a quadratic model is used to reconstruct an image that will visually be under normal illumination. In order to avoid the effect of light source intensity on face images, we used HE method. Experimental results show that, by...
A new incremental face recognition system
, Article 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, Dortmund, 6 September 2007 through 8 September 2007 ; 2007 , Pages 335-340 ; 1424413486 (ISBN); 9781424413485 (ISBN) ; Ghavami, A ; Abrishami Moghaddam, H ; Sharif University of Technology
2007
Abstract
In this paper, we present new adaptive linear discriminant analysis (LDA) algorithm and apply them for adaptive facial feature extraction. Adaptive nature of the proposed algorithm is advantageous for real world applications in which one confronts with a sequence of data such as online face recognition and mobile robotics. Application of the new algorithm on feature extraction from facial image sequences is given in three steps: i) adaptive image preprocessing, ii) adaptive dimension reduction and iii) adaptive LDA feature estimation. Steps 1 and 2 are done simultaneously and outputs of stage 2 are used as a sequence of inputs for stage3. The proposed system was tested on Yale and PIE face...
Feature Extraction in Subspace Domain for Face Recognition
,
Ph.D. Dissertation
Sharif University of Technology
;
Manzuri Shalmani, Mohammad Taghi
(Supervisor)
Abstract
Feature extraction in subspace domain for face recognition has attracted growing attention in recent years. Face image shown by a long vector usually belongs to a manifold of intrinsically low dimension. Researchers in face recognition field try to extract these manifolds using algebraic and statistical tools. Recently, the use of multilinear algebra and multidimensional data in various stages of feature extraction and recognition is considered. This approach reduces small sample size problem and computational cost by considering the spatial information in the image. Although these successes, the performance of the methods based of this idea in term of recognition rate in the applications...
Face Recognition in Subspace Domain Based on Kernel Methods
,
M.Sc. Thesis
Sharif University of Technology
;
Manzuri Shalmani, Mohammad Taghi
(Supervisor)
Abstract
Linear dimension reduction is one of the common methods in face recognition. But this method is not efficient in cases which borders of different classes are nonlinear. In these cases dimension reduction increases the error of recognition significantly. In the problem of face recognition, there are several factors which make the borders of classes nonlinear including variation in illumination, position and expression of the face. So nonlinear methods has been proposed for face recognition in the presence of nonlinear factors. One of theses nonlinear methods is "Kernel" trick. In the Kernel method data is transferred to the new space with a nonlinear mapping. This mapping should be chosen...
Face Recognition Improvement Using Boosting Method
, M.Sc. Thesis Sharif University of Technology ; Kasaei, Shohreh (Supervisor)
Abstract
Biometrics has been long known to recognize persons based on their physical and behavioral characteristics. Face recognition (FR) is one of such biometrics that has received a considerable attention in recent years both from the industry and research communities. As the boosting framework has shown good performance in face recognition, it has been adopted in this work. This thesis deals with pattern recognition methods such as linear discriminant analysis (LDA) and machine learning approaches such as boosting which are integrated to overcome the technical limitation of existing FR methods. However, LDA-based methods often suffer from the so-called “small-sample-size” (SSS) problem arising...
Face Recognition Networks Review and Analysis
, Ph.D. Dissertation Sharif University of Technology ; Razvan, Mohammad Reza (Supervisor) ; Moghadasi, Reza (Supervisor) ; Kamali Tabrizi, Mostafa (Co-Supervisor)
Abstract
Face recognition, which is one of the most important biometrics, has always been one of the main challenges in many security issues, such as verifying the identity of customers of financial institutions and passengers at the airport, and such issues have many applications in daily life. Face recognition has always been an important issue in computer vision and pattern recognition. Currently, several methods based on deep networks have shown great results in face recognition, among which the following can be mentioned.1.The deep face was introduced by Facebook in 2014; 2.Face-net was presented by Google in 2015 ;3.VGGFace was presented by Oxford University in 2015; 4.Openface was presented by...
Simultaneous recognition of facial expression and identity via sparse representation
, Article 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 ; 2014 , Pages 1066-1073 ; ISBN: 9781479949854 ; Fatemizadeh, E ; Mahoor, M. H ; Sharif University of Technology
Abstract
Automatic recognition of facial expression and facial identity from visual data are two challenging problems that are tied together. In the past decade, researchers have mostly tried to solve these two problems separately to come up with face identification systems that are expression-independent and facial expressions recognition systems that are person-independent. This paper presents a new framework using sparse representation for simultaneous recognition of facial expression and identity. Our framework is based on the assumption that any facial appearance is a sparse combination of identities and expressions (i.e., one identity and one expression). Our experimental results using the CK+...
Facial mark detection and removal using graph relations and statistics
, Article 2017 25th Iranian Conference on Electrical Engineering, ICEE 2017, 2 May 2017 through 4 May 2017 ; 2017 , Pages 2223-2228 ; 9781509059638 (ISBN) ; Jamzad, M ; Sharif University of Technology
Abstract
Face Analysis is an important task in image processing. Most of these tasks centralized on face recognition and detection. One of different ways for deceiving automatic face analysis systems is mark notation on the skin. On the other hand some applications attempts to eliminate defects of the face. Hence, in this paper we try to detect and remove skin marks on the face, whether they're natural or not. Our algorithm passes face image through appropriate filters to get mark candidates and then create a graph space using 8-point neighborhood relations of mark candidates image pixels. Then we compute probabilities of each mark candidate using four measures based on intensity of occurrence, shape...
A constructive genetic algorithm for LBP in face recognition
, Article 3rd International Conference on Pattern Analysis and Image Analysis, IPRIA 2017, 19 April 2017 through 20 April 2017 ; 2017 , Pages 182-188 ; 9781509064540 (ISBN) ; Shouraki, S. B ; Sharif University of Technology
Abstract
LBP coefficients are essential and determine the priority of gray differences. The objectives of this paper are to reveal this and propose a method for finding an optimal priority through the genetic algorithm. On the other hand, the genetic operators such as initialization and cross-over operators, generate invalid coefficients, defective chromosomes. This paper also recommends a rectifying method for correcting defective chromosomes. Results on the FERET and Extended Yale B datasets indicate that the proposed method has markedly higher recognition rates than LBP. © 2017 IEEE