Sharif Digital Repository

A major challenge in automatic irrigation of extensive agricultural fields is large scale soil moisture monitoring. Proper orthogonal decomposition (POD) is a widespread data-driven dimension reduction technique which can be combined with QR pivoting method for estimation of high-dimensional signals and optimal sensor placement. However, it requires computation of tailored basis functions which should be extracted from known training data. This is feasible for problems with constant features such as face recognition. However, using fixed bases (and probably fixed sensor selection) may not be an appropriate approach for estimation of signals with time-variant features. This paper demonstrates... 

Given the increasing need for the creation and development of automated systems, the problem of detecting and identifying the faces of people in the images has been considered by the researchers. In recent years, the sparse representation based classification has been of great interest to researchers. The goal of this investigation is to provide a quick and effective way to identify faces based on the sparse representation. Since the basis of sparse representation is to calculate it through L1-norm optimization for high dimensional dictionary with high computational volume, a smoothed L0-norm optimization-based method was introduced. At the time of obtaining the sparse representation using... 

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... 

In the past few years, the application of surveillance for security and smart cities are growing rapidly. The human detection based on the surveillance videos is a complex task and traditional clothing such as headscarf makes this task even more difficult. The surveillance systems designed for many countries are required to be able to recognize the people with these traditional clothing. In this paper, a computer vision system for partially covered face detection in low resolution surveillance videos containing traditional Middle Eastern clothing including the headscarf is presented. The proposed framework uses a combination of Haar cascade and Locally Binary Patterns Histogram (LBPH) for... 

This paper proposes a fusion-based gender recognition method which uses facial images as input. Firstly, this paper utilizes pre-processing and a landmark detection method in order to find the important landmarks of faces. Thereafter, four different frameworks are proposed which are inspired by state-of-the-art gender recognition systems. The first framework extracts features using Local Binary Pattern (LBP) and Principal Component Analysis (PCA) and uses back propagation neural network. The second framework uses Gabor filters, PCA, and kernel Support Vector Machine (SVM). The third framework uses lower part of faces as input and classifies them using kernel SVM. The fourth framework uses... 

The variation of pose, illumination, and expression continues to make face recognition a challenging problem. As a pre-processing step in holistic approaches, faces are usually aligned by eyes. The proposed method tries to perform a pixel alignment rather than eye alignment by mapping the geometry of faces to a reference face while keeping their own textures. The proposed geometry alignment not only creates a meaningful correspondence among every pixel of all faces, but also removes expression and pose variations effectively. The geometry alignment is performed pixel-wise, i.e., every pixel of the face is corresponded to a pixel of the reference face. In the proposed method, the information... 

Reciprocal interaction and facial expression are some of the most interesting topics in the fields of social and cognitive robotics. On the other hand, children with autism show a particular interest toward robots, and facial expression recognition can improve these children’s social interaction abilities in real life. In this research, a robotic platform has been developed for reciprocal interaction consisting of two main phases, namely as Non-structured and Structured interaction modes. In the Non-structured interaction mode, a vision system recognizes the facial expressions of the user through a fuzzy clustering method. The interaction decision-making unit is combined with a fuzzy finite... 

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.... 

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  

One of the main issues in the field of social and cognitive robotics is the robot’s ability to recognize emotional states and emotional interaction between robots and humans. Through effective emotional interaction, robots will be able to perform many tasks in human society. In this research, we have developed a robotic platform and a vision system to recognize the emotional state of the user through its facial expressions, which leads to a more realistic humanrobot interaction (HRI). First, a number of features are extracted according to points detected by a vision system from the face of the user. Then, the emotional state of the user is analyzed with the help of these features. For the... 

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... 

Automatic measurement of spontaneous facial action units (AUs) defined by the facial action coding system (FACS) is a challenging problem. The recent FACS user manual defines 33 AUs to describe different facial activities and expressions. In spontaneous facial expressions, a subset of AUs are often occurred or activated at a time. Given this fact that AUs occurred sparsely over time, we propose a novel method to detect the absence and presence of AUs and estimate their intensity levels via sparse representation (SR). We use the robust principal component analysis to decompose expression from facial identity and then estimate the intensity of multiple AUs jointly using a regression model... 

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... 

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper... 

Facial expression recognition is a challenging and interesting problem in computer vision and pattern recognition. Geometric variability in both emotion expression and neutral face is a fundamental challenge in facial expression recognition problem. This variability not only directly affects geometric facial expression recognition methods, but also is a critical problem in appearance methods. To overcome this problem, this paper presents an approach which eliminates geometric variability in emotion expression; thus, appearance features can be accurately used for facial expression recognition. Therefore, a fixed geometric model is used for geometric normalization of facial images. This model... 

Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami operator which is also used in this paper for hippocampal shape comparison. We proposed a feature vector which consists of size measures and shape descriptors based on Laplace Beltrami eigenvalues and eigenfunctions. The aforementioned feature space is utilised for automatic differentiating normal subjects from epileptic patients as well as distinguishing epileptic patients with left temporal lobe epilepsy (LTLE) from patients with right temporal lobe epilepsy (RTLE). Achieved results are diagnostic accuracy of 93% with 95% sensitivity and lateralization... 

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... 

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... 

In this paper a holistic method and a local method based on decision template ensemble are investigated. In addition by combining both methods, a new hybrid method for boosting the performance of the system is proposed and evaluated with respect to robustness against small sample size problem in face recognition. Inadequate and substantial variations in the available training samples are the two challenging obstacles in classification of an unknown face image. At first in this novel multi learner framework, a decision template is designed for the global face and a set of decision templates is constructed for each local part of the face as a complement to the previous part. The prominent... 

Multi view imaging has attracted increasing attention recently and has become one of the potential avenues in future video systems. We aim to make more reliable and robust automatic feature extraction and natural 3D feature construction from 2D features detected on a pair of frontal and profile view face images. We propose several heuristic algorithms to minimize possible errors introduced by prevalent non-perfect orthogonal condition and non-coherent luminance. In our approach, we first extract the 2D features that are visible to both cameras in both views. Then, we estimate the coordinates of the features in the hidden profile view based on the visible features extracted in the two...