Mrinal Kanti Bhowmik

Thermal Infrared Face Recognition – A Biometric Identification Technique for Robust Security system

Face of an individual is a biometric trait that can be used in computer-based automatic security system for identification or authentication of that individual. While recognizing a face through a machine, the main challenge is to accurately match the input human face with the face image of the same person already stored in the face-database of the system. Not only the computer scientists, but the neuroscientists and psychologists are also taking their interests in the field of development and improvement of face recognition. Numerous applications of it relate mainly to the field of security. Having so many applications of this interesting area, there are challenges as well as pros and cons of the systems. Face image of a subject is the basic input of any face recognition system. Face images may be of different types like visual, thermal, sketch and fused images. A face recognition system suffers from some typical problems. Say for example, visual images result in poor performance with illumination variations, such as indoor and outdoor lighting conditions, low lighting, poses, aging, disguise etc. So, the main aim is to tackle all these problems to give an accurate automatic face recognition. These problems can be solved using thermal images and also using fused images of visual and thermal images. The image produced by employing fusion method provides the combined information of both the visual and thermal images and thus provides more detailed and reliable information which helps in constructing more efficient face recognition system. Objective of this chapter is to introduce the role of different IR spectrums, their applications, some interesting critical observations, available thermal databases, review works, some experimental results on thermal faces as well as on fused faces of visual and thermal face images in face recognition field; and finally sorting their limitations out.

Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition

This paper presents a novel approach to handle the challenges of face recognition. In this work thermal face images are considered, which minimizes the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Polar images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 97.05%.

Breast abnormality detection through statistical feature analysis using infrared thermograms

The utilization of medical infrared thermography in breast abnormality detection is mostly due to its radiation-free, non-invasive and painless nature. Infrared breast thermography is an alternative breast imaging modality that can detect those tumors or early changes which are undetectable by the gold standard method X-ray mammography. However, breast cancer is a highly treatable disease, with 97% chances of survival if getting detected earlier. Thus, early detection of breast cancer using infrared breast thermography may improve the survival rate of breast cancer patients. The temperature pattern in both breasts of a healthy breast thermogram is closely symmetrical. Hence, a small asymmetry in the temperature pattern of the left and right breast may signify a breast abnormality. There are a series of texture features that play a vital role in asymmetry analysis of breast thermograms. This paper mainly emphasizes on investigating those statistical features, which can adequately differentiate the healthy breast thermograms from pathological breast thermograms. A survey work on texture features used by various authors for asymmetry detection is provided in this work. Our analysis is performed on 30 healthy and 30 abnormal breast thermograms of existing DMR (Database for Mastology Research) Database. The analysis and experimental results show that among the first order statistical features, the mean difference, skewness, entropy and standard deviation are the most efficient features that contribute most towards the asymmetry detection.

Optimum fusion of visual and thermal face images for recognition

In this paper one investigation has been done to find the optimum level of fusion to find a fused image from visual as well as thermal images. Because of the use of face recognition system in critical areas like, authenticating an authorized person in highly secured areas, investigation of criminals, online monitoring etc, face recognition system should be very robust and accurate one. This work is an attempt to fuse visual and thermal face images at optimum level to extract the advantages of visual as well as thermal images. In our work, Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database has been used for the visual and thermal images. Among all the experiments a maximum recognition result obtained is 93%.

Classification of fused images using radial basis function neural network for human face recognition

Here an efficient fusion technique for automatic face recognition has been presented. Fusion of visual and thermal images has been done to take the advantages of thermal images as well as visual images. By employing fusion a new image can be obtained, which provides the most detailed, reliable, and discriminating information. In this method fused images are generated using visual and thermal face images in the first step. In the second step, fused images are projected into eigenspace and finally classified using a radial basis function neural network. In the experiments Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark for thermal and visual face images have been used. Experimental results show that the proposed approach performs well in recognizing unknown individuals with a maximum success rate of 96%.

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.

DeitY-TU face database: its design, multiple camera capturing, characteristics, and evaluation

Abstract. The development of the latest face databases is providing researchers different and realistic problems that play an important role in the development of efficient algorithms for solving the difficulties during automatic recognition of human faces. This paper presents the creation of a new visual face database, named the Department of Electronics and Information Technology-Tripura University (DeitY-TU) face database. It contains face images of 524 persons belonging to different nontribes and Mongolian tribes of north-east India, with their anthropometric measurements for identification. Database images are captured within a room with controlled variations in illumination, expression, and pose along with variability in age, gender, accessories, make-up, and partial occlusion. Each image contains the combined primary challenges of face recognition, i.e., illumination, expression, and pose. This database also represents some new features: soft biometric traits such as mole, freckle, scar, etc., and facial anthropometric variations that may be helpful for researchers for biometric recognition. It also gives an equivalent study of the existing two-dimensional face image databases. The database has been tested using two baseline algorithms: linear discriminant analysis and principal component analysis, which may be used by other researchers as the control algorithm performance score.

Polar fusion technique analysis for evaluating the performances of image fusion of thermal and visual images for human face recognition

This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images obtained after fusion of visual and thermal images whereas in second method fusion is applied on log-polar transformed individual visual and thermal images. After this step, which is thus obtained in one form or another, Principal Component Analysis (PCA) is applied to reduce dimension of the fused images. Log-polar transformed images are capable of handling complicacies introduced by scaling and rotation. The main objective of employing fusion is to produce a fused image that provides more detailed and reliable information, which is capable to overcome the drawbacks present in the individual visual and thermal face images. Finally, those reduced fused images are classified using a multilayer perceptron neural network. The database used for the experiments conducted here is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. The second method has shown better performance, which is 95.71% (maximum) and on an average 93.81% as correct recognition rate.

Pain related inflammation analysis using infrared images

Medical Infrared Thermography (MIT) offers a potential non-invasive, non-contact and radiation free imaging modality for assessment of abnormal inflammation having pain in the human body. The assessment of inflammation mainly depends on the emission of heat from the skin surface. Arthritis is a disease of joint damage that generates inflammation in one or more anatomical joints of the body. Osteoarthritis (OA) is the most frequent appearing form of arthritis, and rheumatoid arthritis (RA) is the most threatening form of them. In this study, the inflammatory analysis has been performed on the infrared images of patients suffering from RA and OA. For the analysis, a dataset of 30 bilateral knee thermograms has been captured from the patient of RA and OA by following a thermogram acquisition standard. The thermograms are pre-processed, and areas of interest are extracted for further processing. The investigation of the spread of inflammation is performed along with the statistical analysis of the pre-processed thermograms. The objectives of the study include: i) Generation of a novel thermogram acquisition standard for inflammatory pain disease ii) Analysis of the spread of the inflammation related to RA and OA using K-means clustering. iii) First and second order statistical analysis of pre-processed thermograms. The conclusion reflects that, in most of the cases, RA oriented inflammation affects bilateral knees whereas inflammation related to OA present in the unilateral knee. Also due to the spread of inflammation in OA, contralateral asymmetries are detected through the statistical analysis.

A review on inflammatory pain detection in human body through infrared image analysis

Temperature difference in the skin surface reflects the abnormality present in the human body. Considering the phenomenon, detection and forecasting the change of temperature is the principal objective of using Medical Infrared Thermography (MIT) as a diagnostic tool for inflammatory pain diseases. Medical Infrared Thermography (MIT) is a non-invasive, non-contact and fast imaging technique that record and monitor the flow of body temperature by receiving the infrared emitted from the skin surface. Based on the standardization of thermogram acquisition and processing techniques and by the adoption of advanced infrared cameras, presently it is feasible to detect the minor temperature difference of the skin surface in the high-resolution infrared images. Recently, the research on inflammatory pain detection using medical infrared thermography concentrated on the area of temperature and statistical analysis based automated detection of abnormality from the thermograms. The paper introduces a significant review focusing on the area of different inflammatory pain detection using infrared thermography along with the environmental condition, protocol selection, and acquisition system specification in summarized tabular format. Based on the rigorous study of the publications in the area of inflammatory pain thermography, the paper also explores the area of thermogram processing and analysis of pain in a review work format.