A Face Tracking Algorithm for User Interaction in Mobile Devices

Bulbul, Abdullah; Cipiloglu, Zeynep; Çapın, Tolga

doi:10.1109/cw.2009.9

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…In addition, as we focus on the interaction, we do not specifically look for a robust tracking under extreme or varying lighting conditions. Some clues on how to implement a robust face tracker can be found in [8], [14] and [27].…”

Section: Face Trackingmentioning

confidence: 99%

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Using the user's point of view for interaction on mobile devices

Francone

Nigay

2011

Proceedings of the 23rd Conference on l'Interaction Homme-Machine

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RESUMENous présentons une technique d'interaction pour dispositifs mobiles (smartphone et tablette) basée sur le suivi du visage de l'utilisateur. Cette technique définit de nouvelles possibilités pour l'interaction en entrée et en sortie sur dispositifs mobiles. En sortie, le suivi de la tête peut permettre de contrôler le point de vue sur une scène 3D affichée à l'écran (Head-Coupled Perspective, HCP). Cette technique améliore l'interaction en sortie en offrant la perception de la profondeur et en permettant la visualisation d'un espace de travail plus grand (fenêtre virtuelle). En entrée, le suivi des mouvements de la tête définit une nouvelle modalité d'interaction qui ne requiert pas d'autres capteurs que la caméra du téléphone ou de la tablette. Dans cet article, nous explicitons les possibilités interactionnelles offertes par le suivi de la tête de l'utilisateur sur téléphones ou tablettes, particulièrement adapté au caractère mobile des dispositifs visés. Nous focalisons ensuite sur l'interaction en sortie en présentant plusieurs applications du HCP et en décrivant les résultats d'une expérimentation qualitative sur téléphone et tablette. ABSTRACTWe study interaction modalities for mobile devices (smartphones and tablets) that rely on a camera-based head tracking. This technique defines new possibilities for input and output interaction. For output, by computing the position of the device according to the user's head, it is for example possible to realistically control the viewpoint on a 3D scene (Head-Coupled Perspective, HCP). This technique improves the output interaction bandwidth by enhancing the depth perception and by allowing the visualization of large workspaces (virtual window). For input, head movement can be used as a means of interacting with a mobile device. Moreover such an input modality does not require any additional sensor except the built-in front-facing camera. In this paper, we classify the interaction possibilities offered by head tracking on smartphones and tablets. We then focus on the output interaction by introducing several applications of HCP on both smartphones and tablets and by presenting the results of a qualitative user experiment.

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Section: Face Trackingmentioning

confidence: 99%

“…On mobile devices, most research focuses on defining efficient and robust algorithms [8] [14] for head tracking (with varying lighting conditions and low computational cost), but only a few use it for HCP. pCubee [31] is a device that arranges five small screens into a small box shape and combines it with HCP technique.…”

Section: Related Workmentioning

confidence: 99%

Using the user's point of view for interaction on mobile devices

Francone

Nigay

2011

Proceedings of the 23rd Conference on l'Interaction Homme-Machine

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“…Due to the low computational complexity and the possibility of use on the mobile devices, the face tracking methods based on the skin color classification are important [1][4] [5] [14]. Such a classifiers extract from the RGB space (or other color space) a subspace containing the RGB values corresponding to the possible shades of the skin color.…”

Section: Related Workmentioning

confidence: 99%

Fast GPU and CPU computing for Head Position Estimation

Szkudlarek¹,

Pietruszka²

2015

Annals of Computer Science and Information Systems

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Abstract-The head movement based control methods in the 3D graphic applications requires the real-time face position estimation. Therefore, the tracking method at the high speed and with the minimal latency is needed. This is especially hard to achieve when the face is tracked with the use of the high resolution video image on mobile devices. In the article, we present several methods for an acceleration of the face position estimation method based on the fuzzy skin color classifier and other color-based face tracking methods. The acceleration is achieved through a highly parallel GPU computation, the precalculation of the classifier weights and through the combined computations on the GPU and the CPU. The achieved computation time is independent of the used skin color classification method, allowing for use of very complex classifiers. The presented methods provides the robust head position tracking on the high resolution video image of 1920x1080 pixels, at 300 frames per second, on the mobile device with a low computing power.

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“…For instance, the front camera of a mobile device is generally directed towards its single user. Owing to these issues, an initial attempt is performed in one of our previous studies [4]. In this paper, we extend the previous algorithm to include face tracking in 3D.…”

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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A Face Tracking Algorithm for User Interaction in Mobile Devices

Cited by 8 publications

References 9 publications

Using the user's point of view for interaction on mobile devices

Using the user's point of view for interaction on mobile devices

Fast GPU and CPU computing for Head Position Estimation

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

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