Eye-Blink Controlled Human-Computer Interface for the Disabled

Królak, Aleksandra; Strumiłło, Paweł

doi:10.1007/978-3-642-03202-8_10

Cited by 26 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most eye-blink detection techniques are in fact methods detecting eye regions in images. Many approaches are used for this purpose, such as template matching [10], skin color models [11], projection [12], directional Circle Hough Transform [13], multiple Gabor response waves [14] or eye detection using Haarlike features [15,16].…”

Section: Previous Workmentioning

confidence: 99%

Eye-blink detection system for human–computer interaction

Królak

Strumiłło

2011

Univ Access Inf Soc

188

109

View full text Add to dashboard Cite

A vision-based human-computer interface is presented in the paper. The interface detects voluntary eyeblinks and interprets them as control commands. The employed image processing methods include Haar-like features for automatic face detection, and template matching based eye tracking and eye-blink detection. Interface performance was tested by 49 users (of which 12 were with physical disabilities). Test results indicate interface usefulness in offering an alternative mean of communication with computers. The users entered English and Polish text (with average time of less than 12s per character) and were able to browse the Internet. The interface is based on a notebook equipped with a typical web camera and requires no extra light sources. The interface application is available on-line as open-source software.

show abstract

Section: Previous Workmentioning

confidence: 99%

Eye-blink detection system for human–computer interaction

Królak

Strumiłło

2011

Univ Access Inf Soc

188

109

View full text Add to dashboard Cite

show abstract

“…Much research is under way into means of enabling the disabled to communicate effectively with a computer [ 1 , 2 ], as development of such means has the potential to enhance their quality of life considerably. Depending on users’ capabilities, systems such as speech recognition, brain-computer interfaces [ 3 ], and infrared head-operated joysticks [ 4 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

Barea

Boquete

Rodríguez-Ascariz

et al. 2010

Sensors

View full text Add to dashboard Cite

This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes.

show abstract

“…Hence, researchers defined a rough area in face to reduce the search space for the eye regions [20]. Let the width and the height of the face image be w and h, respectively.…”

Section: Eye Region Decisionmentioning

confidence: 99%

An Efficient and Effective Framework for Eye Region Detection and Eye State Recognition

Chiang

2014

AIVP

View full text Add to dashboard Cite

This paper proposes a framework to treat the eye information in a face image including the eye region detection and the eye state recognition. In order to make it possible to employ our approach to a real application in practice, our goal in this paper is to design not only a fast enough but also a high performance framework for the eye region detection and the eye state recognition. Our proposed framework mainly contains two parts: the first is to locate the eye regions in a face image, and the second is to recognize the states, either open or closed, of the eye regions. When a frame is captured from a video sequence, a face detection method is first performed to determine the positions of face regions. Next, face regions are converted into binary images and then we perform the horizontal and the vertical projections to locate the eye regions. Two visual features containing the intensity values of pixels and local binary pattern (LBP) are extracted from eye regions to classify the eye states with the support vector machine (SVM) approach. This paper also demonstrates a several experiments to present the efficiency and effectiveness of our proposed framework.

show abstract

Eye-Blink Controlled Human-Computer Interface for the Disabled

Cited by 26 publications

References 9 publications

Eye-blink detection system for human–computer interaction

Eye-blink detection system for human–computer interaction

Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

An Efficient and Effective Framework for Eye Region Detection and Eye State Recognition

Contact Info

Product

Resources

About