Blood Perfusion Models for Infrared Face Recognition

Wu, Shiqian; Fang, Zhijun; Xie, Zhihua; Liang, Wei

doi:10.5772/6401

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…The IR data in this paper were captured by an IR camera Thermo Vision A40 supplied by FLIR Systems Inc [4]. The training database comprises 500 thermal images of 50 individuals which were carefully collected under the similar conditions in November 17, 2006: environment under air-conditioned control with temperature around 25.6 26.3 .…”

Section: Resultsmentioning

confidence: 99%

A novel infrared face recognition method in DCT domain

Xie

Liu

et al. 2010

2010 International Conference on Wavelet Analysis and Pattern Recognition

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A novel infrared face recognition method using blood perfusion conversion and DCT is proposed in this paper. Firstly, the blood perfusion image instead of thermal image is used to get the stable biological features.Secondly, due to the low-resolution of the infrared images, the feature extraction method (Linear Discriminative Analysis in DCT domain) is chosen to get the principle features in the blood perfusion image, which is suitable for real-time application. A new feature selection algorithm in DCT domain is applied to lessen the small sample problem of LDA. The experiments illustrate that the feature selection algorithm in DCT domain is effective in getting the discriminant features and the proposed method has better performance compared with the traditional DCT and LDA method.

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Section: Resultsmentioning

confidence: 99%

A novel infrared face recognition method in DCT domain

Xie

Liu

et al. 2010

2010 International Conference on Wavelet Analysis and Pattern Recognition

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“…Objects emit different amounts of IR energy according to their body temperature and characteristics. Previously, thermal IR camera was costly, but recently the cost of such cameras has come down considerably with the development of CCD technology [ 7 ]. Thermal images can be captured under different lighting conditions, even under completely dark environment.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Human Thermal Face Recognition Based on Haar Wavelet Transform and Local Binary Pattern

Bhattacharjee

Seal

Ganguly

et al. 2012

Computational Intelligence and Neuroscience

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Thermal infrared (IR) images focus on changes of temperature distribution on facial muscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face two recognition methods working in thermal spectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands subimages are created for each face image. Then a total confidence matrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of subimages, each of size 8 × 8 pixels. For each such subimages, LBP features are extracted which are concatenated in manner. PCA is performed separately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feed forward neural network and minimum distance classifier are used to classify face images. The experiments have been performed on the database created at our own laboratory and Terravic Facial IR Database.

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“…Recently researchers have been using Near-IR imaging cameras for face recognition with better results [2]. Previously Thermal IR camera was costly but recently the cost of IR cameras has been considerably reduced with the development of CCD technology [3]; thermal images can be captured under different lighting conditions, even under completely dark environment. Using thermal images, the tasks of face detection, localization, and segmentation are comparatively easier and more reliable than those in visible band images [4].…”

Section: Introductionmentioning

confidence: 99%

Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm

Seal

Gangulyb

Bhattacharjee

et al. 2012

Procedia Technology

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In this paper, a thermal infra red face recognition system for human identification and verification using blood perfusion data and back propagation feed forward neural network is proposed. The system consists of three steps. At the very first step face region is cropped from the colour 24-bit input images. Secondly face features are extracted from the croped region, which will be taken as the input of the back propagation feed forward neural network in the third step and classification and recognition is carried out. The proposed approaches are tested on a number of human thermal infra red face images created at our own laboratory. Experimental results reveal the higher degree performance.

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Blood Perfusion Models for Infrared Face Recognition

Cited by 8 publications

References 17 publications

A novel infrared face recognition method in DCT domain

A novel infrared face recognition method in DCT domain

A Comparative Study of Human Thermal Face Recognition Based on Haar Wavelet Transform and Local Binary Pattern

Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm

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