Search for: linear-discriminant-analysis
Total 31 records
Article 3rd Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2017, 20 December 2017 through 21 December 2017 ; Volume 2017-December , December , 2018 , Pages 141-146 ; 9781538649725 (ISBN) ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc 2018
In this paper, a frame-based method with reference frame was proposed to recognize six basic facial emotions (anger, disgust, fear, happy, sadness and surprise) and also neutral face. By using face landmarks, a fast algorithm was used to calculate an appropriate descriptor for each frame. Furthermore, Linear Discriminant Analysis (LDA) was used to reduce the dimension of defined descriptors and to classify them. The LDA problem was solved using the least squares solution and Ledoit-Wolf lemma. The proposed method was also compared with some studies on CK+ dataset which has the best accuracy among them. To generalize the proposed method over CK+ dataset, a landmark detector was needed....
Article Computing and Informatics ; Volume 30, Issue 5 , 2011 , Pages 965-986 ; 13359150 (ISSN) ; Manzuri Shalmani, M. T ; Sharif University of Technology
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...
Article Proceedings of SPIE - The International Society for Optical Engineering, 9 December 2011 through 10 December 2011 ; Volume 8349 , December , 2012 ; 0277786X (ISSN) ; 9780819490254 (ISBN) ; Baseri Salehi, N ; Kasaei, S ; Sharif University of Technology
A new face recognition method is proposed in this paper. The proposed method is based on fuzzy regularized linear discriminant analysis (FR-LDA) and combines the regularized linear discriminant analysis (R-LDA) and the fuzzy set theory. 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. In the proposed method, we calculate the membership degree matrix by Fuzzy K-nearest neighbor (FKNN) and then incorporate the membership degree into the definition of the between-class and within-class scatter matrices and get the fuzzy between-class and within-class scatter...
M.Sc. Thesis Sharif University of Technology ; Kasaei, Shohreh
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...
Article 2009 3rd Asia International Conference on Modelling and Simulation, AMS 2009, Bandung, Bali, 25 May 2009 through 26 May 2009 ; 2009 , Pages 131-135 ; 9780769536484 (ISBN) ; Asgarian, E ; Zanjani, M ; Rezaee, A ; Seidi, M ; Universitas Katolik Parahyangan; Nottingham Trent University; UKSim; IEEE Computer Society; Asia Modelling and Simulation Society, AMSS ; Sharif University of Technology
An appropriate pre-processing algorithm in classification is not only of great importance with respect to classifier choice, but also would be more crucial. In this paper, a pre-processing step is proposed in order to increase accuracy of classification. The aim of this approach is finding a transformation matrix causes classes to be more discriminable by transforming data into the new space and consequently, increases the classification accuracy. This transformation matrix is computed through two methods based on linear discrimination. In the first method, we use class independent LDA to increase classification accuracy by finding a transformation that maximizes the between-class scatter...
Article 2009 14th International CSI Computer Conference, CSICC 2009, 20 October 2009 through 21 October 2009, Tehran ; 2009 , Pages 658-663 ; 9781424442621 (ISBN) ; Kasaei, S ; Sharif University of Technology
Discriminative subspace analysis is a popular approach for a variety of applications. There is a growing interest in subspace learning techniques for face recognition. Principal component analysis (PCA) and eigenfaces are two important subspace analysis methods have been widely applied in a variety of areas. However, the excessive dimension of data space often causes the curse of dimensionality dilemma, expensive computational cost, and sometimes the singularity problem. In this paper, a new supervised discriminative subspace analysis is presented by encoding face image as a high order general tensor. As face space can be considered as a nonlinear submanifold embedded in the tensor space, a...
Article IEEE Sensors Journal ; 2017 ; 1530437X (ISSN) ; Mohammadzade, H ; Mokari, M ; Sharif University of Technology
This article proposes a new method for viewinvariant action recognition that utilizes the temporal position of skeletal joints obtained by Kinect sensor. In this method, the actions are represented as sequences of several pre-defined poses. After pre-processing, which includes skeleton alignment and scaling, the appropriate feature vectors are obtained for recognizing and discriminating the pose of every frame by the proposed Fisherposes method. The proposed regularized Mahalanobis distance metric is used in order to recognize both the involuntary and highly made-up actions at the same time. Hidden Markov Model (HMM) is then used to classify the action related to an input sequence of poses....
M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi
Today we live in an era of continuous technology improvement which results in huge changes in different areas of diverse industries. Among the most recent systems for monitoring and quality control which benefits from high speed, are machine vision systems. The output of these systems, are digital images that can be used for monitoring instead of the original products. Unfortunately due to the computational complexity of data extracted from the digital images, traditional methods lose their efficiency. Therefore, in this thesis, a method is proposed to design a model for the monitoring and control of image-based processes, which uses classification methods, that are capable of classifying...
Design of a Colorimetric Sensor Array Based on Aggregation of Plasmonic Nanoparticles for Detection and discrimination of Phosalone, Thiometon and Prothioconazole Pesticides, M.Sc. Thesis Sharif University of Technology ; Hormozi Nezhad, Mohammad Reza ; Ghasemi, Forough
The use of myriad pesticides in agriculture crops has promoted the demand of developing rapid and sensitive probes for the detection and discrimination of diverse pesticides, which are usually used together. In this work, a colorimetric sensor array has been designed for identification and discrimination of Thiometon (TM) and Phosalone (PS) as organophosphate pesticides and Prothioconazole (PC) as a triazole pesticide. For this purpose, two different plasmonic nanoparticles including unmodified gold nanoparticles (AuNPs) and unmodified silver nanoparticles (AgNPs) were used as sensing elements. The principle of the proposed strategy relied on the aggregation of AuNPs and AgNPs through the...
M.Sc. Thesis Sharif University of Technology ; Rafiee, Majid
Nowadays, electrical energy is one of the crucial requirements in human beings’ lives. Since the majority of the world’s energy is met by fossil fuel, the problems such as global warming, the reduction of fossil fuel resources, and unpredictable oscillation of the prices of such fuels have led to a serious crisis for the people of the world. Furthermore, due to the increasing energy demand, the economic development of most countries has a strong correlation with fossil fuel prices. The abovementioned problems have made a lot of countries take alternative policies in terms of generating energy, one of which is using renewable energy resources. This approach is reported to be clean and...
Article European Signal Processing Conference ; 10 November 2014 , 2014 , pp. 775-779 ; ISSN: 22195491 ; ISBN: 9780992862619 ; Joneidi, M ; Sadeghi, M ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
A new algorithm for learning jointly reconstructive and discriminative dictionaries for sparse representation (SR) is presented. While in a usual dictionary learning algorithm like K-SVD only the reconstructive aspect of the sparse representations is considered to learn a dictionary, in our proposed algorithm, which we call K-LDA, the discriminative aspect of the sparse representations is also addressed. In fact, K-LDA is an extension of K-SVD in the case that the class informations (labels) of the training data are also available. K-LDA takes into account these information in order to make the sparse representations more discriminate. It makes a trade-off between the amount of...
Article Eurasip Journal on Advances in Signal Processing ; Volume 2011 , 2011 ; 16876172 (ISSN) ; Manzuri Shalmani, M. T ; Sharif University of Technology
Motivated by the fact that in computer vision data samples are matrices, in this paper, we propose a matrix-variate probabilistic model for canonical correlation analysis (CCA). Unlike probabilistic CCA which converts the image samples into the vectors, our method uses the original image matrices for data representation. We show that the maximum likelihood parameter estimation of the model leads to the two-dimensional canonical correlation directions. This model helps for better understanding of two-dimensional Canonical Correlation Analysis (2DCCA), and for further extending the method into more complex probabilistic model. In addition, we show that two-dimensional Linear Discriminant...
Non-speaker information reduction from Cosine Similarity Scoring in i-vector based speaker verification, Article Computers and Electrical Engineering ; Volume 48 , November , 2015 , Pages 226–238 ; 00457906 (ISSN) ; Mirian, A ; Sameti, H ; BabaAli, B ; Sharif University of Technology
Elsevier Ltd 2015
Cosine similarity and Probabilistic Linear Discriminant Analysis (PLDA) in i-vector space are two state-of-the-art scoring methods in speaker verification field. While PLDA usually gives better accuracy, Cosine Similarity Scoring (CSS) remains a widely used method due to simplicity and acceptable performance. In this domain, several channel compensation and score normalization methods have been proposed to improve the performance. We investigate non-speaker information in cosine similarity metric and propose a new approach to remove it from the decision making process. I-vectors hold a large amount of non-speaker information such as channel effects, language, and phonetic content. This type...
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
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...
Article Analytical Chemistry ; Volume 88, Issue 16 , 2016 , Pages 8099-8106 ; 00032700 (ISSN) ; Hormozi Nezhad, M. R ; Sharif University of Technology
American Chemical Society
There is a growing interest in developing high-performance sensors monitoring organophosphate pesticides, primarily due to their broad usage and harmful effects on mammals. In the present study, a colorimetric sensor array consisting of citrate-capped 13 nm gold nanoparticles (AuNPs) has been proposed for the detection and discrimination of several organophosphate pesticides (OPs). The aggregation-induced spectral changes of AuNPs upon OP addition has been analyzed with pattern recognition techniques, including hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). In addition, the proposed sensor array has the capability to identify individual OPs or mixtures of them in...
Article 26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019, 27 November 2019 through 28 November 2019 ; 2019 , Pages 172-176 ; 9781728156637 (ISBN) ; Hajipour Sardouie, S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc 2019
Detection of seizure periods in an epileptic patient is an important part of health care. However, due to the variety in types of seizures and location of them, real-time seizure detection is not straight forward. In this paper, we propose a method for seizure detection from EEG signals in datasets which have both generalized and focal seizures. The proposed method is useful in the situations that we have no prior knowledge about the location of the patient's seizure and the pattern of evolution of seizure location. In the proposed method, first, the artifacts are automatically reduced by Blind Source Separation (BSS) methods. Then, the channels are clustered into two clusters. After that,...
Development of a colorimetric sensor array based on monometallic and bimetallic nanoparticles for discrimination of triazole fungicides, Article Analytical and Bioanalytical Chemistry ; April , 2021 ; 16182642 (ISSN) ; Fahimi Kashani, N ; Hormozi Nezhad, M. R ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH 2021
Due to the widespread use of pesticides and their harmful effects on humans and wildlife, monitoring their residual amounts in crops is critically essential but still challenging regarding the development of high-throughput approaches. Herein, a colorimetric sensor array has been proposed for discrimination and identification of triazole fungicides using monometallic and bimetallic silver and gold nanoparticles. Aggregation-induced behavior of AgNPs, AuNPs, and Au-AgNPs in the presence of four triazole fungicides produced a fingerprint response pattern for each analyte. Innovative changes to the metal composition of nanoparticles leads to the production of entirely distinct response patterns...
Article Scientia Iranica ; Volume 26, Issue 1 , 2019 , Pages 72-94 ; 10263098 (ISSN) ; Mohammadzade, H ; Chamanzar, A ; Shabany, M ; Ghojogh, B ; Sharif University of Technology
Sharif University of Technology 2019
Brain Computer Interface (BCI) systems, which are based on motor imagery, enable humans to command artificial peripherals by merely thinking about the task. There is a tremendous interest in implementing BCIs on portable platforms, such as Field Programmable Gate Arrays (FPGAS) due to their low-cost, low-power and portability characteristics. This article presents the design and implementation of a Brain Computer Interface (BCI) system based on motor imagery on a Virtex-6 FPGA. In order to design an accurate algorithm, the proposed method avails statistical learning methods such as Mutual Information (MI), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). It also uses...
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
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...
M.Sc. Thesis Sharif University of Technology ; Mohammadzadeh, Hoda
The human action recognition is one of the most important concepts of computer vision in recent decades. Most of the two dimensional methods in this field are facing serious challenges such as occlusion and missing the third dimension of data. Development of depth sensors has made easy access to tracking people and 3D positions of human body joints. This Thesis proposes a new method of action recognition that utilizes the position of joints obtained by Kinect sensor. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space. Two types of distances, i.e., Euclidean and Mahalanobis, are used for recognizing the states. Also, Hidden Markov Model...