Optimized Visual and Thermal Image Fusion for Efficient Face Recognition

Hanif, Muhammad; Ali, Usman

doi:10.1109/icif.2006.301735

Cited by 27 publications

(15 citation statements)

References 18 publications

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“…: as the first step in the process of face recognition in biometric systems [2,5,14,17,18,31] where the main task could be localization of the face features for comparison and recognition. Moreover, it can be used to improve the methods of face analysis in the visible light [1,4,10,12,36] to enable observation in poor illumination and finally in the design of computer-human interface [7,20] to track human faces in infrared images. Source image and segmentation results for selected cases.…”

Section: Comparison With Other Authors' Resultsmentioning

confidence: 99%

Automatic method for detection of characteristic areas in thermal face images

Marzec

Koprowski

Wróbel

et al. 2013

Multimed Tools Appl

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The use of thermal images of a selected area of the head in screening systems, which perform fast and accurate analysis of the temperature distribution of individual areas, requires the use of profiled image analysis methods. There exist methods for automated face analysis which are used at airports or train stations and are designed to detect people with fever. However, they do not enable automatic separation of specific areas of the face. This paper presents an algorithm for image analysis which enables localization of characteristic areas of the face in thermograms. The algorithm is resistant to subjects' variability and also to changes in the position and orientation of the head. In addition, an attempt was made to eliminate the impact of background and interference caused by hair and hairline. The algorithm automatically adjusts its operation parameters to suit the prevailing room conditions. Compared to previous studies (Marzec et al., J Med Inform Tech 16:151-159, 2010), the set of thermal images was expanded by 34 images. As a result, the research material was a total of 125 patients' thermograms performed in the Department of Pediatrics and Child and Adolescent Neurology in Katowice, Poland. The images were taken interchangeably with several thermal cameras: AGEMA 590 PAL (sensitivity of 0.1°C), ThermaCam S65 (sensitivity of 0.08°C), A310 (sensitivity of 0.05°C), T335 (sensitivity of 0.05°C) with a 320×240 pixel optical resolution of detectors, maintaining the principles related to taking thermal images for medical thermography. In comparison to (Marzec et al., J Med Inform Tech 16:151-159, 2010), the approach presented there has been extended and modified. Based on the comparison with other methods presented in the literature, it was demonstrated that this method is more complex as it enables to determine the approximate areas of selected parts of Multimed Tools Appl (2015)

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Section: Comparison With Other Authors' Resultsmentioning

confidence: 99%

Automatic method for detection of characteristic areas in thermal face images

Marzec

Koprowski

Wróbel

et al. 2013

Multimed Tools Appl

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“…[3], [4], since thermal images solve some of the difficulties of this problem by providing relatively illumination invariant face images. Some approaches to surveillance-type tracking of moving targets include [5], and [1].…”

Section: A Fusion Of Thermal and Visible Datamentioning

confidence: 99%

Bayesian fusion of thermal and visible spectra camera data for region based tracking with rapid background adaptation

Rees¹,

Talha²,

Florescu

2012

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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This paper presents a method for optimally combining pixel information from an infra-red thermal imaging camera, and a conventional visible spectrum colour camera, for tracking a moving target. The tracking algorithm rapidly re-learns its background models for each camera modality from scratch at every frame. This enables, firstly, automatic adjustment of the relative importance of thermal and visible information in decision making, and, secondly, a degree of "camouflage target" tracking by continuously re-weighting the importance of those parts of the target model that are most distinct from the present background at each frame. Furthermore, this very rapid background adaptation ensures robustness to large, sudden and arbitrary camera motion, and thus makes this method a useful tool for robotics, for example visual servoing of a pan-tilt turret mounted on a moving robot vehicle. The method can be used to track any kind of arbitrarily shaped or deforming object, however the combination of thermal and visible information proves particularly useful for enabling robots to track people. The method is also important in that it can be readily extended for data fusion of an arbitrary number of statistically independent features from one or arbitrarily many imaging modalities.

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“…To overcome this, Hanif et al [6] have proposed data fusion methods by taking some percentage of the pixel information from the visual and corresponding thermal image to fuse an image. Gyaourova et al [18] tried to implement pixel based fusion scheme in the wavelet domain, and feature based fusion in the eigenspace domain.…”

Section: Introductionmentioning

confidence: 99%

“…Various approaches applied for various applications in recent years have helped in developing many algorithms and image fusion software. Recently, researchers have investigated the use of fusion of thermal infrared and visual face images for person identification to tackle the drawbacks of individual thermal and visual images [5], [6], [7], [8], and [9]. So far, research work on fusion has been carried out for years.…”

Section: Introductionmentioning

confidence: 99%

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

Bhowmik

Bhattacharjee

et al. 2012

2012 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

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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.

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Optimized Visual and Thermal Image Fusion for Efficient Face Recognition

Cited by 27 publications

References 18 publications

Automatic method for detection of characteristic areas in thermal face images

Automatic method for detection of characteristic areas in thermal face images

Bayesian fusion of thermal and visible spectra camera data for region based tracking with rapid background adaptation

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

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