Blink and click

Siriluck, W.; Kamolphiwong, Sinchai; Kamolphiwong, Thossaporn; Sae-Whong, S.

doi:10.1145/1328491.1328503

Cited by 7 publications

(7 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%

“…Hunke et al also propose an image-based neural networks algorithm that performs face tracking for human-computer interaction [13]. The Blink and Click project is another approach, where the traditional mouse is replaced by human facial actions [17]. For instance, the nose location is used as the mouse pointer and left/right eye blinks correspond to left/right mouse clicks.…”

Section: Related Workmentioning

confidence: 99%

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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%

Section: Related Workmentioning

confidence: 99%

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

2010

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“…The second type, 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 [9], [10]. Image based methods are popular in Computer Vision due to their robustness.…”

Section: Related Workmentioning

confidence: 99%

“…Hunke et al proposes an image based neural networks algorithm that performs face tracking for human computer interaction [9]. Blink and Click project is another application, where traditional mouse is replaced by human facial actions [10]. For instance, nose location is used as the mouse pointer and left/right eye blinks correspond to left/right mouse clicks, in this system.…”

Section: Related Workmentioning

confidence: 99%

A Face Tracking Algorithm for User Interaction in Mobile Devices

Bulbul

Cipiloglu

Çapın

2009

2009 International Conference on CyberWorlds

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Abstract-A new face tracking algorithm, and a human-computer interaction technique based on this algorithm, are proposed for use on mobile devices. The face tracking algorithm considers the limitations of mobile use case -constrained 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. The face tracking system generates 2D face position as an output that can be used for controlling different applications. Two of such applications are also presented in this work; the first example uses face position to determine the viewpoint, and the second example enables an intuitive way of browsing large images.

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“…Some of the techniques used for object acquisition are: eye gaze tracking [1], [2], face tracking [3], [4], facial feature tracking [5], scanning [6]- [9], and tongue movement [10]. Object selection can be performed by using key trigger [11], eye blinking [12], [13], dwell time trigger [14], [15], antisaccades, gaze gestures, on-off screen buttons, dashers, pEYEs [16], mouth opening click [17], tooth clicker [2], brows up clicker [8], EMG clicking [18], and clicking with smiling [19]. The major deciding factors for selection of a combination of object acquisition and selection techniques are: physical condition of a user, cost, performance, and ease of use.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Object Acquisition Using Automatic Scanning and Facial Feature Tracking

Singh

2018

ijarcs

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This paper presents a comparison of object acquisition in an HCI system using two different techniques: automatic scanning and facial feature tracking. In automatic scanning the focus moves from one object to next object automatically after a predefined time period called scanning time. Automatic scanning has been implemented by using MATLAB algorithm which virtually activates the tab key after each scanning time and the focus moves from object to object. The user activates a selection trigger for selection of the object when the focus comes over the object of interest. Whereas, in facial feature tracking approach the mouse cursor is moved in proportion to the movement of user's face. To implement this technique Camera Mouse has been used which requires a simple webcam. It continuously takes facial images of the user and finds the mouse cursor position from the face coordinates. The two techniques are compared based upon accuracy and acquisition time for acquisition of text and graphic objects.

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Blink and click

Cited by 7 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

A Face Tracking Algorithm for User Interaction in Mobile Devices

Performance Comparison of Object Acquisition Using Automatic Scanning and Facial Feature Tracking

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