Automatic Feature Localization in Thermal Images for Facial Expression Recognition

Trujillo, Leonardo; Olague, Gustavo; Hammoud, Riad I.; Hernández, Benjamín

doi:10.1109/cvpr.2005.415

Cited by 75 publications

(43 citation statements)

References 15 publications

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“…The authors in publication [19] used a combined edge and corner detector, called the Harris operator, for the initial detection of interesting eye points, which enabled to single out groups of pixels located in the areas where the biggest changes in the image brightness occurred. These groups were located in the eye and mouth areas.…”

Section: Related Workmentioning

confidence: 99%

“…In [18,19], the authors used simple information based on edges and corners. In article [13] the highest values of pixels were sufficient owing to the use of IR illumination.…”

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%

“…They can be also used in the creation of human-computer interfaces whose task is to facilitate cooperation with the computer [3] or increase safety [6]. 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].…”

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: Related Workmentioning

confidence: 99%

“…In [18,19], the authors used simple information based on edges and corners. In article [13] the highest values of pixels were sufficient owing to the use of IR illumination.…”

Section: Selection and Generation Of Feature Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…SVMs have been widely used in the literature to model classification problems including facial expression recognition [30], [34], [19]. Provided a set of training samples, an SVM transforms the data samples using a non-linear mapping to a higher dimension with the aim to determine a hyperplane that partitions the data by class or labels.…”

Section: Algorithm 1: Compute Hdtpmentioning

confidence: 99%

Modeling Stress Using Thermal Facial Patterns: A Spatio-temporal Approach

Sharma

Dhall

Gedeon

et al. 2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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Abstract-Stress is a serious concern facing our world today, motivating the development of better objective understanding using non-intrusive means for stress recognition. The aim for the work was to use thermal imaging of facial regions to detect stress automatically. The work uses facial regions captured in videos in thermal (TS) and visible (VS) spectra and introduces our database ANU StressDB. It describes the experiment conducted for acquiring TS and VS videos of observers of stressed and not-stressed films for the ANU StressDB. Further, it presents an application of local binary patterns on three orthogonal planes (LBP-TOP) on VS and TS videos for stress recognition. It proposes a novel method to capture dynamic thermal patterns in histograms (HDTP) to utilise thermal and spatio-temporal characteristics associated in TS videos. Individual-independent support vector machine classifiers were developed for stress recognition. Results show that a fusion of facial patterns from VS and TS videos produced significantly better stress recognition rates than patterns from only VS or TS videos with p < 0.01. The best stress recognition rate was 72% and it was obtained from HDTP features fused with LBP-TOP features for TS and VS videos, respectively.

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