Face locating and tracking for human-computer interaction

Hunke, M.; Waibel, Alex

doi:10.1109/acssc.1994.471664

Cited by 73 publications

(38 citation statements)

References 1 publication

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“…The second type of solutions, which contains image-based methods, is based on scanning the image through a window to find the face candidates. The methods in this category generally use template matching, support vector machines (SVM), or neural networks [13,17]. Image-based methods are popular in tracking due to their robustness.…”

Section: Related Workmentioning

confidence: 99%

A color-based face tracking algorithm for enhancing interaction with mobile devices

2010

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A color-based face tracking algorithm is proposed to be used as a human-computer interaction tool on mobile devices. The solution provides a natural means of interaction enabling a motion parallax effect in applications. The algorithm considers the characteristics of mobile useconstrained computational resources and varying environmental conditions. The solution is based on color comparisons and works on images gathered from the front camera of a device. In addition to color comparisons, the coherency of the facial pixels is considered in the algorithm. Several applications are also demonstrated in this work, which use the face position to determine the viewpoint in a virtual scene, or for browsing large images. The accuracy of the system is tested under different environmental conditions such as lighting and background, and the performance of the system is measured in different types of mobile devices. According to these measurements the system allows for accurate (7% RMS error) face tracking in real time (20-100 fps).

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Section: Related Workmentioning

confidence: 99%

A color-based face tracking algorithm for enhancing interaction with mobile devices

2010

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“…In critical care patients there is the possibility of paler skin due to their severe illness. However, measurements have shown that paler skin has almost the same chromaticity as yellowish or dark skin [34][35][36] thus presenting no further significant difficulty. Skin can also show up more white than usual due to changes in light, but this effect is normalized out by taking colour ratios and is shown to have minimal effect on skin detection [36][37][38].…”

Section: Skin Recognitionmentioning

confidence: 99%

Measuring facial grimacing for quantifying patient agitation in critical care

Becouze

Hann

Chase

2007

Computer Methods and Programs in Biomedicine

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The effective delivery of sedation in critical care relies primarily on an accurate and consistent measure of a patient's agitation level. However, current methods for assessing agitation are subjective and prone to error, often leading to over sedation or cycles between agitation and oversedation. This paper builds on previous work developing agitation sensors based on heart rate and blood pressure variability, and overall whole body motion. In this research, the focus is on real-time measurement of high resolution facial changes that are observed to occur in agitation. An algorithm is developed that measures the degree of facial grimacing from a single digital camera.The method is demonstrated on simulated patient facial motion to prove the concept.A consistent measure is obtained that is robust to significant random head movement and compares well against visual observation of different levels of grimacing. The method provides a basis for clinical validation.

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“…To find robots in a camera image, our approach first filters the image using Gaussian color filters tuned to the colors of the markers (see e.g., [34]). The center of the colors are then obtained by local smoothing, and thresholding is applied to determine whether or not a robot can be seen in the image.…”

Section: Detectionmentioning

confidence: 99%

A Monte Carlo Algorithm for Multi-Robot Localization

Fox¹,

Burgard²,

Kruppa³

et al. 1999

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This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots, using computer vision and laser range finding for detecting each other and estimating each other's relative location. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.

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Face locating and tracking for human-computer interaction

Cited by 73 publications

References 1 publication

A color-based face tracking algorithm for enhancing interaction with mobile devices

A color-based face tracking algorithm for enhancing interaction with mobile devices

Measuring facial grimacing for quantifying patient agitation in critical care

A Monte Carlo Algorithm for Multi-Robot Localization

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