Gaze direction estimation by component separation for recognition of Eye Accessing Cues

Vranceanu, Ruxandra; Florea, Corneliu; Florea, Laura; Vertan, Constantin

doi:10.1007/s00138-014-0656-8

Cited by 16 publications

(6 citation statements)

References 31 publications

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“…The computational complexity is very less; we achieved frame rates around 24 Hz in Python implementation in a 2.0 GHz Core i5 PC running Ubuntu 64 bit (4GB RAM). The per-frame computational time is 42 ms, which is much less than that of the other state of the art methods (250 ms in [20]). Off the shelf webcams can be used for computing the Eye gaze direction.…”

Section: Discussionmentioning

confidence: 96%

“…From the confusion matrix, it can be seen that most of the mix classifications are in differentiating between right, down right, etc. The classification accuracy is poor in the vertical direction (As observed in [20]). This can be attributed to the lack of spatial resolution in the vertical direction.…”

Section: Discussionmentioning

confidence: 97%

“…Recently Vrânceanu et al [20] proposed another approach for eye direction detection using component separation. Iris, sclera, and skin are segmented and the features obtained are used in a machine learning framework for classifying the eye gaze direction.…”

Section: Related Workmentioning

confidence: 99%

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Real-time eye gaze direction classification using convolutional neural network

George

Routray

2016

2016 International Conference on Signal Processing and Communications (SPCOM)

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Estimation eye gaze direction is useful in various human-computer interaction tasks. Knowledge of gaze direction can give valuable information regarding users point of attention. Certain patterns of eye movements known as eye accessing cues are reported to be related to the cognitive processes in the human brain. We propose a real-time framework for the classification of eye gaze direction and estimation of eye accessing cues. In the first stage, the algorithm detects faces using a modified version of the Viola-Jones algorithm. A rough eye region is obtained using geometric relations and facial landmarks. The eye region obtained is used in the subsequent stage to classify the eye gaze direction. A convolutional neural network is employed in this work for the classification of eye gaze direction. The proposed algorithm was tested on Eye Chimera database and found to outperform state of the art methods. The computational complexity of the algorithm is very less in the testing phase. The algorithm achieved an average frame rate of 24 fps in the desktop environment.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 97%

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Real-time eye gaze direction classification using convolutional neural network

George

Routray

2016

2016 International Conference on Signal Processing and Communications (SPCOM)

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“…Recently another approach using component separation to detect eye direction is proposed by Vranceanu et al [14]. A Machine Learning framework is proposed to classify the gaze direction for Iris, sclera, and skin.…”

Section: Related Workmentioning

confidence: 99%

Effect of DCT Image Compression on Eye Gaze Direction Detection

et al. 2018

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Abstract. The prediction of eye direction detection is the one of the popular research topic in human computer interaction area. This paper defines eye gaze detection by using Discrete Cosine Transform. Actually, determining the position of eyes is difficult to estimate the location of gaze which is more challenging. The database of the suggested research is organized as gaze directions of right, left and centre. The database is created with varied ages of images. In this paper, Discrete Cosine Transform has been applied to the image database and the effect of image compression is tested by using back propagation neural networks.

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“…In this neuro-linguistic programming, the concept of visual accessing cues is introduced for an application for eye feature analysis (Brandler & Grinder, 1999). The positions of the iris can be used as indicators for the internal representational system for which part of the brain is active during the mental process (Vrânceanu, Florea, Florea, & Vertan, 2015). Furthermore, biometric-based method of identification has the lowest error rate that leads to a good reliability for iris recognition (Shi et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Personality Prediction Based on Iris Position Classification Using Support Vector Machines

Ramli¹,

Nordin²

2018

IJEECS

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<p>Predicting personality generally involves personal interpretations of a person which makes the current methods for personality prediction process less adequate, timely and tedious. Thus, a simple yet efficient alternative method is proposed in this project for detecting iris positions which are used in Neuro-Linguistic Programming as clues for the human internal representational system and mental activity. This study set out to determine several positions of the iris of a person based on the Eye Accessing Cues. The design and the development of a complete system will be undertaken as for the users to use as a medium to predict their personality based on their iris position. Several pre-processing techniques were executed to each of the data before run into the testing and training activities for accuracy gaining. The algorithm used for classification of the positions is Support Vector Machine which by taking rectangle crop of an eye with 9000 pixels as inputs. Radial Basis Function is used for the kernel parameter of the proposed method. The classification will then map into the type of a person with the lists of his personality based on Visual, Auditory and Kinaesthetic theory. The result of the classification of the iris positions is currently 84.9% accurate which in the future might be increased by tuning several other parameters that consisted in Support Vector Machine.</p>

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Gaze direction estimation by component separation for recognition of Eye Accessing Cues

Cited by 16 publications

References 31 publications

Real-time eye gaze direction classification using convolutional neural network

Real-time eye gaze direction classification using convolutional neural network

Effect of DCT Image Compression on Eye Gaze Direction Detection

Personality Prediction Based on Iris Position Classification Using Support Vector Machines

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