Eye Localization from Infrared Thermal Images

Wang, Shangfei; Shen, Peijia; Liu, Zhilei

doi:10.1007/978-3-642-37081-6_5

Cited by 11 publications

(20 citation statements)

References 10 publications

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“…To measure the effectiveness of the proposed approach, the authors used a method proposed by Martinez [8] and used by Wang [20]. The displacement error between the manually selected and automatically detected characteristic points of the eyes was defined as (6):…”

Section: Comparison With Other Authors' Resultsmentioning

confidence: 99%

“…||.|| represents L2 norm. The authors in [8,20] assumed the value of err eye < 0.15 as correct localization. The values of err eye were calculated for each analysed image (original 199 images without any transformation) and are presented in Fig.…”

Section: Comparison With Other Authors' Resultsmentioning

confidence: 99%

“…In publication [20] the authors proposed an automatic eye localization method for subjects with and without eyeglasses. For the subjects wearing eyeglasses, the locations of valleys in the vertical and horizontal projection of curves were used to perform eye localization.…”

Section: Related Workmentioning

confidence: 99%

“…By contrast, in [8] the set of features was prepared by the Haar wavelet transform and required more calculations. In publication [20], the authors used two sets of features. The first was based on the parameters calculated from the image projection and the second one was based on Haar-like features.…”

Section: Selection and Generation Of Feature Valuesmentioning

confidence: 99%

“…A particularly important element of the face are the eyes because they carry a large amount of information [7,19]. Therefore, many described solutions are directly related to eye localization [18,8,13,20,21] or eye localization is part of a larger system of analysis of the entire face [1,10,14,19]. The algorithm for eye localization from thermal images presented in this paper enables to automate this process.…”

Section: Introductionmentioning

confidence: 99%

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Fast eye localization from thermal images using neural networks

Marzec

Lamża

Wróbel

et al. 2016

Multimed Tools Appl

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The paper presents a fast algorithm for eye localization from thermal images. Due to blurred edges, lower quality of thermal images compared with visible-light images, the process may be more complicated. On the other hand, it seems relatively easy to designate specific areas of the eyes because of their relatively highest brightness resulting from the highest temperature in these face areas. It turns out, however, that the highest pixel brightness does not always determine unequivocally the position of the eyes. Therefore, the algorithm described here proposes a specialized method for segmentation of images containing eye areas and a specialized classifier. The first part of the algorithm is a block for generating features describing eye areas. The second stage is a decision block built based on a neural network allowing for effective classification of pre-designated areas. A suitable combination of these blocks into a single system allowed for correct analysis of images with rich geometric diversity (scale, position, orientation) and brightness distribution, reaching more than 91 % of correct localizations and time of analysis of a single image of hundredths of a second. Algorithms of this type can be used as the initial stages of facial recognition, emotion recognition, quantitative analysis of the face or modern human-computer interfaces. The described algorithm may be used for both conventional thermal images and thermograms (after prior conversion of temperature values into brightness values), which affects its potential application in medicine. The benefits of this study are twofold: fast localization of characteristic points of the eyes and the possibility of processing despite the typical problems of thermovision and image transformations.

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Section: Comparison With Other Authors' Resultsmentioning

confidence: 99%

Section: Comparison With Other Authors' Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Selection and Generation Of Feature Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fast eye localization from thermal images using neural networks

Marzec

Lamża

Wróbel

et al. 2016

Multimed Tools Appl

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Fast eyes detection in thermal images

Knapik

Cyganek

2020

Multimed Tools Appl

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In recent years many methods have been proposed for eye detection. In some cases however, such as driver drowsiness detection, lighting conditions are so challenging that only the thermal imaging is a robust alternative to the visible light sensors. However, thermal images suffer from poor contrast and high noise, which arise due to the physical properties of the long waves processing. In this paper we propose an efficient method for eyes detection based on thermal image processing which can be successfully used in challenging environments. Image pre-processing with novel virtual high dynamic range procedure is proposed, which greatly enhances thermal image contrast and allows for more reliable computation of sparse image descriptors. The bag-of-visual-words approach with clustering was selected for final detections. We compare our method with the YOLOv3 deep learning model. Our method attains high accuracy and fast response in real conditions without computational complexity and requirement of a big dataset associated with the deep neural networks. For quantitative analysis a series of thermal video sequences were recorded in which eye locations were manually annotated. Created dataset was made publicly available on our website.

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Optimization Method of IR Thermography Facial Image Registration

et al. 2019

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So far, there have been many types of researches subject to technical requirements due to image registration. Using image registration can lower deviation from sequential images and make it possible to analyze the information variation of the particular area subsequently. This study provides a procedure creating fixed image based on the data of facial IR thermography, where its methods include the visual saliency map by detected image, as well as cluster algorithm. Comparison is also made here to solve the merits and demerits by affine parameter to reach the optimum measure among Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing Algorithm, where there are two control parameters concerning this experiment: one is the calculation of time confined each alignment, while the other one is to use parallel computing toolbox or not. The optimum method will be chosen by the values of the objective function based on the control parameters. Afterward, the optimal internal parameter is to be verified through the Taguchi experiment and the validity of this procedure in this study will be built following the parameter result as above. Therefore, the difference of images before and after alignment can be validated by overlapping the images before and after alignment as well as the image quality measurements, where its results reveal that the alignment procedure of IR thermography in this study is capable of performing human face alignment precisely, and subsequently, do help data statistics and analysis concerning temperature area interdependence.

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Eye Localization from Infrared Thermal Images

Cited by 11 publications

References 10 publications

Fast eye localization from thermal images using neural networks

Fast eye localization from thermal images using neural networks

Fast eyes detection in thermal images

Optimization Method of IR Thermography Facial Image Registration

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