A deep facial landmarks detection with facial contour and facial components constraint

Baddar, Wissam J.; Son, Jisoo; Kim, Dae Hoe; Kim, Seong-Tae; Ro, Yong Man

doi:10.1109/icip.2016.7532952

Cited by 17 publications

(7 citation statements)

References 24 publications

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“…Despite being advantageous over the single mode imaging, the visible-thermal imaging system had been scarcely reported for recognition of face and facial tissue in breathing estimation applications, 28,40 due in part to the relatively complicated imaging architecture and data processing. Although there exist more effective and efficient approaches to find the face region in the face detection domain, 41,42 the proposed face and facial tissue detection method can achieve the acceptable accuracy for breathing measurement using triple coordinate calculation operations based on the traditional algorithm, thus having met the objectives of the current study. Consequently, considering the results and discussion mentioned above, we state that the visible and thermal dual-mode imaging framework and related algorithm in this study offer an alternative or complementary solution to face and nose as well as mouth detection in breathing research.…”

Section: Detection Accuracy Of Face and Facial Tissuementioning

confidence: 99%

Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement

Zhai

et al. 2017

J. Biomed. Opt.

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We present a dual-mode imaging system operating on visible and long-wave infrared wavelengths for achieving the noncontact and nonobtrusive measurements of breathing rate and pattern, no matter whether the subjects use the nose and mouth simultaneously, alternately, or individually when they breathe. The improved classifiers in tandem with the biological characteristics outperformed the custom cascade classifiers using the Viola–Jones algorithm for the cross-spectrum detection of face and nose as well as mouth. In terms of breathing rate estimation, the results obtained by this system were verified to be consistent with those measured by reference method via the Bland–Altman plot with 95% limits of agreement from ? 2.998 to 2.391 and linear correlation analysis with a correlation coefficient of 0.971, indicating that this method was acceptable for the quantitative analysis of breathing. In addition, the breathing waveforms extracted by the dual-mode imaging system were basically the same as the corresponding standard breathing sequences. Since the validation experiments were conducted under challenging conditions, such as the significant positional and abrupt physiological variations, we stated that this dual-mode imaging system utilizing the respective advantages of RGB and thermal cameras was a promising breathing measurement tool for residential care and clinical applications.

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Section: Detection Accuracy Of Face and Facial Tissuementioning

confidence: 99%

Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement

Zhai

et al. 2017

J. Biomed. Opt.

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“…Detection of facial landmarks is a big part of pre-processing the data for more accurate training with DNNs. In [13], two types of deep convolutional neural networks (DCNNs) are used in conjunction. Combining these two DCNNs shows good results.…”

Section: Neural Networkmentioning

confidence: 99%

Gesture Interpretation Control System Using Convolutional Neural Networks

Baldursson

Rasti

Gudmundsson

et al. 2019

2019 International Conference on Biomedical Innovations and Applications (BIA)

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This thesis proposes a non-invasive control system for electrical wheelchairs utilizing facial gestures of individuals captured by a real-time monocular camera. The images are interpreted with a convolutional neural network that achieves up to ~99.6% overall accuracy. The control system uses an embedded system with a graphics processing unit for predicting real-time throughput with fast classification time. This solution offers excellent versatility, where the user can make a gesture to depict a command of his choice.

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“…Computer vision research has developed powerful face recognition algorithms trained to place landmarks quickly, automatically, and reproducibly, using regression tree methods (King, 2009). While they have seen extensive use in computer vision work (Baddar et al, 2016;Damer et al, 2019;Özseven & Düğenci, 2017;Schroff et al, 2015), these methods have not yet been validated for use in social perception research. Given that these automatically placed landmarks capture shape information vital for facial recognition (Juhong & Pintavirooj, 2017;Shi et al, 2006), they may capture equally well the metrics of interest to social perception.…”

mentioning

confidence: 99%

Facial metrics generated from manually and automatically placed image landmarks are highly correlated

Jones

Schild

Jones

2021

Evolution and Human Behavior

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Research on social judgments of faces often investigates relationships between measures of face shape taken from images (facial metrics), and either perceptual ratings of the faces on various traits (e.g., attractiveness) or characteristics of the photographed individual (e.g., their health). A barrier to carrying out this research using large numbers of face images is the time it takes to manually position the landmarks from which these facial metrics are derived. Although research in face recognition has led to the development of algorithms that can automatically position landmarks on face images, the utility of such methods for deriving facial metrics commonly used in research on social judgments of faces has not yet been established. Thus, across two studies, we investigated the correlations between four facial metrics commonly used in social perception research (sexual dimorphism, distinctiveness, bilateral asymmetry, and facial width to height ratio) when measured from manually and automatically placed landmarks. In the first study, in two independent sets of open access face images, we found that facial metrics derived from manually and automatically placed landmarks were typically highly correlated, in both raw and Procrustes-fitted representations. In study two, we investigated the potential for automatic landmark placement to differ between White and East Asian faces. We found that two metrics, facial width to height ratio and sexual dimorphism, were better approximated by automatic landmarks in East Asian faces. However, this difference was small, and easily corrected with outlier detection. These data validate the use of automatically placed landmarks for calculating facial metrics to use in VALIDATING AUTOMATIC FACIAL LANDMARKS 3 research on social judgments of faces, but we urge caution in their use. We also provide a tutorial for the automatic placement of landmarks on face images.

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A deep facial landmarks detection with facial contour and facial components constraint

Cited by 17 publications

References 24 publications

Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement

Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement

Gesture Interpretation Control System Using Convolutional Neural Networks

Facial metrics generated from manually and automatically placed image landmarks are highly correlated

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