Eye detection using discriminatory Haar features and a new efficient SVM

Chen, Shuo; Liu, Chengjun

doi:10.1016/j.imavis.2014.10.007

Cited by 62 publications

(22 citation statements)

References 36 publications

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“…Zhang et al [8] use local linear SVM for 65 eye center detection, and ASEF-based filters are applied to select the candidate centers. In [9], the authors apply a discriminative feature extraction method to 2D Haar wavelet transformation [10] and use an efficient SVM for fast classification. Support Vector Regressor (SVR) is used to estimate the distance of patch center to the pupil center by extracted HoG features in [11].…”

mentioning

confidence: 99%

A joint cascaded framework for simultaneous eye detection and eye state estimation

Gou

Wang

et al. 2017

Pattern Recognition

103

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Eye detection and eye state (close/open) estimation are important for a wide range of applications, including iris recognition, visual interaction and driver fatigue detection. Current work typically performs eye detection first, followed by eye state estimation by a separate classifier. Such an approach fails to capture the interactions between eye location and its state. In this paper, we propose a method for simultaneous eye detection and eye state estimation. Based on

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mentioning

confidence: 99%

A joint cascaded framework for simultaneous eye detection and eye state estimation

Gou

Wang

et al. 2017

Pattern Recognition

103

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“…1) Measures of Accuracy for Eye Gaze Tracking Methods: Contemporary research on gaze tracking measures accuracy in a wide variety of ways [9]. For example, commonly used measures include angular resolution in degrees [10], gaze recognition rates in percentage [11], and shifts in number of pixels or distance in cm/mm between gaze [12] and target locations. Unfortunately, these 4 methods are not correlated and not inter-comparable.…”

Section: A Contemporary Methods For Eye Gaze Estimationmentioning

confidence: 99%

“…A comparative evaluation of different classification methods, such as SVM, neural networks, and k-Nearest Neighbor (k-NN)s is presented in [29] where Local Binary Patterns Histograms (LBPH) and Principle Component Analysis (PCA) are used to extract eye appearance features. The use of Haar features is reported in [30], [31] for real time gaze tracking. In [32] a neural network with a skin colour model to detect face and eye regions is used.…”

Section: ) Appearance-based Methodsmentioning

confidence: 99%

Convolutional Neural Network Implementation for Eye-Gaze Estimation on Low-Quality Consumer Imaging Systems

Lemley

Kar

Drîmbarean³

et al. 2019

IEEE Trans. Consumer Electron.

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Accurate and efficient eye gaze estimation is important for emerging consumer electronic systems such as driver monitoring systems and novel user interfaces. Such systems are required to operate reliably in difficult, unconstrained environments with low power consumption and at minimal cost. In this paper a new hardware friendly, convolutional neural network model with minimal computational requirements is introduced and assessed for efficient appearance-based gaze estimation. The model is tested and compared against existing appearance based CNN approaches, achieving better eye gaze accuracy with significantly fewer computational requirements. A brief updated literature review is also provided.

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“…It first extract key features of images to train a model regarding appearance or structures of eye and then fit the learned model to determine eye centers. Many machine learning algorithms have been used for eye center localization such as Bayesian models [20], hidden Markov models (HMMs) [21], support vector machines (SVM) [22,23,24] and AdaBoost [25]. Kim et al [26] localized eye centers using a multi-scale approach, which was based on Gabor vectors.…”

Section: Related Workmentioning

confidence: 99%

Accurate and robust eye center localization via fully convolutional networks

Xia

Wang

2019

IEEE/CAA J. Autom. Sinica

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Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network (FCN), which is an endto-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning. Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

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Eye detection using discriminatory Haar features and a new efficient SVM

Cited by 62 publications

References 36 publications

A joint cascaded framework for simultaneous eye detection and eye state estimation

A joint cascaded framework for simultaneous eye detection and eye state estimation

Convolutional Neural Network Implementation for Eye-Gaze Estimation on Low-Quality Consumer Imaging Systems

Accurate and robust eye center localization via fully convolutional networks

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