Sharif Digital Repository

In recent years, processing the images that contain human faces has been a growing research interest because of establishment and development of automatic methods especially in security applications, compression, and perceptual user interface. In this paper, a new method has been proposed for multiple face detection and tracking in video frames. The proposed method uses skin color, edge and shape information, face detection, and dynamic movement analysis of faces for more accurate real-time multiple face detection and tracking purposes. One of the main advantages of the proposed method is its robustness against usual challenges in face tracking such as scaling, rotation, scene changes, fast... 

The first step of any face processing system is detecting the location in images where faces are present. In this paper we present an upright frontal face detection system based on the multi-resolution analysis of the face. In this method firstly, skin-color information is used to detect skin pixels in color images; then, the skin-region blocks are decomposed into frequency sub-bands using contourlet transform. Features extracted from sub-bands are used to detect face in each block. A multi-layer perceptrone (MLP) neural network was trained to do this classification. To decrease false positive detection we use eyes and lips template matching. These templates achieved by averaging... 

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... 

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... 

In this paper a novel method called Extended Two-Dimensional PCA (E2DPCA) is proposed which is an extension to the original 2DPCA. We state that the covariance matrix of 2DPCA is equivalent to the average of the main diagonal of the covariance matrix of PCA. This implies that 2DPCA eliminates some covariance information that can be useful for recognition. E2DPCA instead of just using the main diagonal considers a radius of r diagonals around it and expands the averaging so as to include the covariance information within those diagonals. The parameter r unifies PCA and 2DPCA. r=1 produces the covariance of 2DPCA, r=n that of PCA. Hence, by controlling r it is possible to control the... 

This paper 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. Our proposed method doesn't use the skin color information for face detection. The detection of faces is accomplished after canceling the effect of the various illumination conditions. Two completely different methods are proposed for face detection in this paper. Because of the independency of the approaches to the face's skin color, the persons with every kind of skin colors are detected even in completely dark environments. Next, the detected faces are... 

In this study, we use direct simulation Monte Carlo method to simulate subsonic flow in nanochannels and micro/nanoscale backward-facing (BF) step considering a wide range of Knudsen number regimes. The nanochannel flow simulation indicates that the nanoscale flow through the nanochannel resembles unique features such as encountering negative pressure deviation behavior and observing flat velocity profiles at higher Knudsen number regimes. On the other hand, the micro/nano BF step flow simulations demonstrate that the length of separation region considerably decreases as the flow becomes more rarefied and approaches the transition regime. Meanwhile, the variations in the flow properties are... 

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... 

Passenger Vehicle Type Choice (PVTC) has an undeniable impact on crash severity and costs. To estimate that, one must carefully consider subjective factors as well as objective ones. Ignoring the role of subjective variables can cause bias in the estimation of the effective variables' coefficients. The present paper investigates the impact of safety attitude, transportation attitude, and lifestyle on PVTC in a bi-level structure. To do so, we used the data from 633 face-to-face interviews conducted in Iran. The passenger vehicles investigated in this study are divided into five categories according to their weight and safety performance: mini, compact, medium, large, and sport utility... 

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... 

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... 

In this paper, we present a novel multilinear algebra based feature extraction approach for face recognition which preserves some implicit structural or locally-spatial information among elements of the original images. We call this method three-dimensional modular discriminant analysis (3DMDA). Our approach uses a new data model called third-order tensor model (3TM) for representing the face images. In this model, each image is partitioned into the several equal size local blocks, and the local blocks are combined to represent the image as a third-order tensor. Then, a new optimization algorithm called direct mode (d-mode) is introduced for learning three optimal projection axes. Extensive... 

This paper 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 paper. 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... 

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... 

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+... 

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...