An improved eye feature extraction algorithm based on deformable templates

Kuo, Paul; Hannah, John

doi:10.1109/icip.2005.1530278

Cited by 16 publications

(14 citation statements)

References 6 publications

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“…Hybrid-based method used the combination of shape and appearance within a single system in eye detection through part-based approaches. Kuo and Hannah [33] proposed a part-based model employing a prior shape model consisting of several subcomponents. Firstly, the eye region is detected using threshold and binary search.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Efficient Iris and Eyelids Detection from Facial Sketch Images

Boonchuan

Setumin

Radman

et al. 2018

ELCVIA

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In this paper, we propose a simple yet effective technique for an automatic iris and eyelids detection method for facial sketch images. Our system uses Circular Hough Transformation (CHT) algorithm for iris localization process and a low level grayscale analysis for eyelids contour segmentation procedure. We limit the input face for the system to facial sketch photos with frontal pose, illumination invariant, neutral expression and without occlusions. CUHK and IIIT-D sketch databases are used to acquire the experimental results. As to validate the proposed algorithm, experiments on ground truth for iris and eyelids segmentation, which are prepared at our lab, is conducted. The iris segmentation from the proposed method gives the best accuracy of 92.93 and 86.71 based on F-measure evaluation for IIIT-D and CUHK, respectively. For eyelids segmentation, on the other hand, the proposed algorithm achieves an average of 4 standard deviation which indicates the closeness of proposed method to ground truth.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Efficient Iris and Eyelids Detection from Facial Sketch Images

Boonchuan

Setumin

Radman

et al. 2018

ELCVIA

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“…Recently, with the aim of applying reference inputs to control systems via gazes, many more studies have been conducted for the purpose of collecting data from the eye region [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Iris and eye corner detection by processing internal webcam images

Turan¹,

Eskikurt²

2016

Turk J Elec Eng & Comp Sci

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Abstract:In this study, the aim is to extract the attributes of the eye regions of laptop users. To achieve this, the iris and eye corners are detected by processing the images captured by the standard internal webcam of a laptop. In addition, an artificial neural network (ANN) is used for determining the eye region. Hereby, the iris and eye corners can be detected in the determined eye region. In the study, 107 user images are captured by using a laptop's internal camera under different light intensities, environments, viewpoints, and positions. These images are used for the training of the ANN. Two different methods are used for the iris detection. In the first, circular Hough transform (CHT) is employed for iris detection in the determined eye region. In the second, the right and the left iris regions are determined by using two different ANNs respectively and then CHT is employed for the iris. Higher success rates are achieved by the second method. In the next stage of the study, two different methods, weighted variance projection (WVPF) function and lowest valued pixels (LVP), are used for the detection of the eye corners. It is demonstrated that the second method has a higher performance than the first.

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“…An image region is the best appropriateness with template (eyes, mouth, etc.) which will minimize the energy [15][16][17]. Advantages of this group method are using template and determining parameter for important components of face, but disadvantage is not to reflect face global structure.…”

Section: The Association Of Geometric Feature-based Methods and Indepementioning

confidence: 99%

Applying Artificial Neural Networks for Face Recognition

2011

Advances in Artificial Neural Systems

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This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN) to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

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An improved eye feature extraction algorithm based on deformable templates

Cited by 16 publications

References 6 publications

Efficient Iris and Eyelids Detection from Facial Sketch Images

Efficient Iris and Eyelids Detection from Facial Sketch Images

Iris and eye corner detection by processing internal webcam images

Applying Artificial Neural Networks for Face Recognition

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