Barin Kumar De

Multisensor fusion of visual and thermal images for human face identification using different SVM kernels

In this paper we present a novel method of face identification using different levels of pixel fusion (e.g. ratios for pixel information taken from the visual and thermal images are, 2:3, 1:1, 3:2 and 7:3) and classification of fused images using different kernels of Support Vector Machine (SVM). Visual imagery has been broadly used in face identification systems, but these are very sensitive to illumination changes. This limitation has been overcome by the Infrared (IR) spectrum that provides simpler and more robust solution to boost the identification performance in uncontrolled environments and deliberate attempts to obscure identity. But IR imagery is sensitive to temperature changes in the surrounding environment and variations in the heat patterns of the face and it is opaque to glass. All these facts degrade the face identification efficiency. This drove us to fuse information from both visual and thermal spectra, which have the potential to improve face identification performance as fusion of thermal and visual images provide improved images with more compact information. Once we get fused images those are reduced in dimension using Eigenvalue Decomposition based Candid Co-variance free Incremental Principal Component Analysis (EVD-CCIPCA) and these reduced fused images are classified using the three different kernels of SVM. The three kernels used here are: linear, polynomial and gaussian RBF. SVM is primarily a classifier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. In this paper, we have used multiclass SVM to carry out identification on face images and Quadratic Programming (QP) optimization method to train the SVM. For experiments, IRIS Thermal/Visual Face Database is used. Experimental results show that 97.28% is the highest average success rate achieved on the fused images of 70% visual and 30% thermal images using the linear kernel. However, the highest success rate of 100% is achieved for classes 4 and 10 in several cases.

A Survey on Imaging-based Breast Cancer Detection

Breast cancer is undoubtedly a dreadful and life-threatening disease. It is fairly common in women and also the second deadliest cancer in the world. It is arguably the most frightening type of cancer because of its well-publicized nature and potential for lethality. If identified and properly treated in its early stage, the chance of cure increases. Different imaging techniques are there which plays a vital role in the detection of breast cancer. In recent days, mammography and thermography are the two main techniques accepted in the medical field to detect breast cancer followed by other screening methods. A literature survey is presented in this paper based on these two techniques followed by the analysis of their affordability, reliability, and outcomes.

Mathematical Representations of Blended Facial Expressions towards Facial Expression Modeling

Abstract The paper mainly aims to create a mapping between facial expression and its corresponding facial muscle contractions along with their movement directions. This mapping is illustrated in terms of mathematical symbolic representations. The paper proposes a set of mathematical representations of basic as well as blended facial expressions. These symbolic representations of facial expressions are evaluated from different normalized facial features and 2D spatial coordinates of a face. They offer a simple generalization of 18 facial expressions and will be used as a background formulation for generating expressive face image from a given neutral face. The facial expression modeling and synthesis is a widely useful application for man-machine interaction.

Expressions Recognition of North-East Indian (NEI) Faces

Facial expression is one of the major distracting factors for face recognition performance. Pose and illumination variations on face images also influence the performance of face recognition systems. The combination of three variations (facial expression, pose and illumination) seriously degrades the recognition accuracy. In this paper, three experimental protocols are designed in such a way that the successive performance degradation due to the increasing variations (expressions, expressions with illumination effect and expressions with illumination and pose effect) on face images can be examined. The whole experiment is carried out using North-East Indian (NEI) face images with the help of four well-known classification algorithms namely Linear Discriminant Analysis (LDA), K-Nearest Neighbor algorithm (KNN), combination of Principal Component Analysis and Linear Discriminant Analysis (PCA + LDA), combination of Principal Component Analysis and K-Nearest Neighbor algorithm (PCA + KNN). The experimental observations are analyzed through confusion matrices and graphs. This paper also describes the creation of NEI facial expression database, which contains visual static face images of different ethnic groups of the North-East states. The database is useful for future researchers in the area of forensic science, medical applications, affective computing, intelligent environments, lie detection, psychiatry, anthropology, etc.

Background Subtraction Algorithm for Moving Object Detection Using SAMEER-TU Dataset

Identifying moving objects plays an important role in video-based applications. In this paper, a background subtraction approach for object detection technique is proposed, which is an improvised version of an existing background subtraction algorithm called visual background extractor (ViBe). Here, the performance of the existing technique has been modified by a median filter. This technique is implemented on different existing databases and also on newly created Society of Applied Microwave Electronics Engineering and Research-Tripura University (SAMEER-TU) dataset. The detection accuracy of the technique is also measured, and a comparison is also carried out between existing and proposed technique, and results are reported in experimental results, in terms of detection accuracy for color video sequence.

Facial component-based blended facial expressions generation from static neutral face images

Facial expression synthesis is getting a wide-spread attention since past several years due to its multimedia applications. In most of the earlier research works, example images of target expressions are required to produce synthesized facial expressions. The paper aims to generate six basic and twelve blended facial expressions from a static and RGB neutral face image without any exemplar of expressive face images. The proposed automatic expression generation system consists of several sub-systems, namely, a knowledge-based system, a module for symbolic formulations of basic and blended facial expressions, an expressive facial components generator and an expressive face generator. The knowledge-based system stores the normalized facial feature parameter values. Symbolic formulations of facial expressions are used to reconstruct facial expressions from a static neutral face image using the parameters stored in the knowledge base. Expressive facial components generator performs automatic expressive facial feature generation as well as automatic facial feature extraction and landmark annotation. Finally, expressive facial components are combined to produce an expressive face in expressive face generator. The system generated expressive face images are validated in four different ways: inter-rater reliability measure, similarity measurement in the frequency domain, similarity measurement using SSIM, FSIM, HOG features and accuracy measurement using both appearance-based and geometry-based feature extraction methods. The geometry based feature extraction method generates 90% recognition accuracy for system generated face images.

Characterization and recognition of mixed emotional expressions in thermal face image

Facial expressions in infrared imaging have been introduced to solve the problem of illumination, which is an integral constituent of visual imagery. The paper investigates facial skin temperature distribution on mixed thermal facial expressions of our created face database where six are basic expressions and rest 12 are a mixture of those basic expressions. Temperature analysis has been performed on three facial regions of interest (ROIs); periorbital, supraorbital and mouth. Temperature variability of the ROIs in different expressions has been measured using statistical parameters. The temperature variation measurement in ROIs of a particular expression corresponds to a vector, which is later used in recognition of mixed facial expressions. Investigations show that facial features in mixed facial expressions can be characterized by positive emotion induced facial features and negative emotion induced facial features. Supraorbital is a useful facial region that can differentiate basic expressions from mixed expressions. Analysis and interpretation of mixed expressions have been conducted with the help of box and whisker plot. Facial region containing mixture of two expressions is generally less temperature inducing than corresponding facial region containing basic expressions.