Facial expression recognition from near-infrared video sequences

Taini, Matti; Zhao, Guoying; Li, S.Z.; Pietikäinen, Matti

doi:10.1109/icpr.2008.4761697

Cited by 40 publications

(20 citation statements)

References 8 publications

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“…Motivated by the successful application of SVM in different vision algorithms [33], [34], we have decided to use it for classifying our features. Two separate SVMs have been used for the classification of the features extracted from the different modalities.…”

Section: Fusing and Classificationmentioning

confidence: 99%

Thermal super-pixels for bimodal stress recognition

Irani

Nasrollahi

Dhall³

et al. 2016

2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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Abstract-Stress is a response to time pressure or negative environmental conditions. If its stimulus iterates or stays for a long time, it affects health conditions. Thus, stress recognition is an important issue. Traditional systems for this purpose are mostly contact-based, i.e., they require a sensor to be in touch with the body which is not always practical. Contact-free monitoring of the stress by a camera [1], [2] can be an alternative. These systems usually utilize only an RGB or a thermal camera to recognize stress. To the best of our knowledge, the only work on fusion of these two modalities for stress recognition is [3] which uses a feature level fusion of the two modalities. The features in [3] are extracted directly from pixel values. In this paper we show that extracting the features from super-pixels, followed by decision level fusion results in a system outperforming [3]. The experimental results on ANUstressDB database show that our system achieves 89% classification accuracy.

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Section: Fusing and Classificationmentioning

confidence: 99%

Thermal super-pixels for bimodal stress recognition

Irani

Nasrollahi

Dhall³

et al. 2016

2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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“…According to a study that investigated facial expression recognition using LBP-TOP features, VS and near-infrared images produced similar facial expression recognition rates provided that VS images had strong illumination [30]. Further, LBP-TOP features may not be able to fully exploit thermal information provided in TS videos because it is different in nature, i.e.…”

Section: Spatio-temporal Features In Thermal and Visible Spectramentioning

confidence: 99%

“…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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“…1602 video sequences from the novel NIR facial expression database [9] were used to recognize six typical expressions: anger, disgust, fear, happiness, sadness and surprise. Video sequences came from 50 subjects, with two to six expressions per subject.…”

Section: Weight Assignment Experimentsmentioning

confidence: 99%

“…Nearinfrared (NIR) imaging (780-1100 nm) is robust to illumination variations, and it has been used successfully for illumination invariant face recognition [8]. Our earlier work shows that facial expression recognition accuracies in different illuminations are quite consistent in the NIR images, while results decrease much in the VL images [9]. Especially for illumination cross-validation, facial expression recognition from the NIR video sequences outperforms VL videos, which provides promising performance for real applications.…”

Section: Introductionmentioning

confidence: 99%

Weight-Based Facial Expression Recognition from Near-Infrared Video Sequences

Taini

Zhao

2009

Image Analysis

Self Cite

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Abstract. This paper presents a novel weight-based approach to recognize facial expressions from the near-infrared (NIR) video sequences. Facial expressions can be thought of as specific dynamic textures where local appearance and motion information need to be considered. The face image is divided into several regions from which local binary patterns from three orthogonal planes (LBP-TOP) features are extracted to be used as a facial feature descriptor. The use of LBP-TOP features enables us to set different weights for each of the three planes (appearance, horizontal motion and vertical motion) inside the block volume. The performance of the proposed method is tested in the novel NIR facial expression database. Assigning different weights to the planes according to their contribution improves the performance. NIR images are shown to deal with illumination variations comparing with visible light images.

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Facial expression recognition from near-infrared video sequences

Cited by 40 publications

References 8 publications

Thermal super-pixels for bimodal stress recognition

Thermal super-pixels for bimodal stress recognition

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

Weight-Based Facial Expression Recognition from Near-Infrared Video Sequences

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