Sharif Digital Repository

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.... 

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,... 

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.... 

This paper proposes a new method for view-invariant 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.... 

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... 

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... 

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...