Eyes extraction from facial images using edge density

Shafi, Muhammad; Chung, Pei-Jung

doi:10.1109/ukricis.2008.4798958

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…The original algorithm proposed in [3] has been modified by incorporating the human visual response. Experimental results are presented to show the improvement with respect to the original algorithm.…”

Section: A New Methods For Hvs Based Eye Detectionmentioning

confidence: 99%

“…Feature vector algorithms that are based on the binary edge map of the image include Cell Edge Distribution (CED) and Principal Projected Edge Distribution (PPED) [1] [2]. Morphological operations are also used on binary edge maps for extraction of essential features from images [3]. The Histogram of Oriented Gradients (HOG) [4] is a method of feature extraction that is based on the gradient image instead of edge maps.…”

Section: Introductionmentioning

confidence: 99%

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Human visual system inspired object detection and recognition

Mandal

Panetta

Agaian

2012

2012 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)

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This paper presents a new generic framework for human visual system inspired object detection and recognition and introduces the idea of feature extraction based on the human visual sensitivity. These methods can greatly enhance robotic vision applications. Additionally a new computationally effective object detection algorithm is presented based on image morphology and visual sensitivity. This new method surpasses the performance of the existing method based on traditional edge detectors. We also present the effectiveness of the algorithm on under-illuminated images.

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Section: A New Methods For Hvs Based Eye Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human visual system inspired object detection and recognition

Mandal

Panetta

Agaian

2012

2012 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)

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“…Using vertical projections, eye locations are determined and light spots are used for the final eye location. Shafi et al [13] combine the work of [25,26], and [27] to create a hybrid eye detection algorithm utilizing illumination, color, and edgedensity information to determine the location of the eyes. Lu et al [28] use rectangular as well as pixel-pattern-based texture features, while Khairosfaizal et al [29] use circular Hough transforms.…”

Section: Introductionmentioning

confidence: 99%

On designing an unconstrained tri-band pupil detection system for human identification

Whitelam

Bourlai

2015

Machine Vision and Applications

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The majority of facial recognition systems depend on the accurate location of both the left and right eye centers in an effort to geometrically normalize the face images available in a database under study. In this paper, we propose a novel pupil detection algorithmic approach that automatically and efficiently locates the eye centers of face images captured using visible and infrared sensors when operating under challenging conditions. Our proposed approach includes the usage of long-range and night-time face images captured in the infrared (IR) band under active illumination. It also efficiently deals with partial face obstruction (subjects wearing eye glasses) as well as face pose and illumination variation. Our scenario-adaptable methodological approach involves a number of algorithmic steps, including (i) situation classification (to automatically determine the acquisition scenario each input face image is coming from), (ii) generation and usage of 2D normalized correlation coefficients, (iii) prediction of eye localization, (iv) computation of summation range filters for accurate pupil detection, and an (v) eye glasses classifier on facial images using support vector machines (to determine when subjects are wearing glasses or not). Our proposed approach is compared against state-of-the-art academic and commercial eye detection algorithms, including the (a) Viola and Jones Adaboost method, (b) one of the latest academic eye detection algorithms proposed by Valenti and Gevers (IEEE TPAMI), and (c) the eye detection approach that is available as part of the G8 commercial face recognition software B Cameron Whitelam (package provided by L1 systems). Experimental results demonstrate that our proposed approach outperforms all other approaches when applied on various challenging face datasets, including IR face images captured behind tinted glass or at long ranges up to about 350 feet away, day or night. We also show the benefits of our approach to face normalization and, as a result, face recognition improvement performance in terms of rank-1 identification rates. This is an important achievement that has practical value for forensic tool operators who may have to manually localize the eye centers of all face images available in a dataset, before further face image preprocessing and face-based matching algorithms can be applied.

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“…However, there is a multitude of work done in automatic eye detection performed in the visible (350nm -850nm) and Near Infrared (NIR -850nm -1050nm) spectra. [8] Face Region X Isophotes Visible Asteriadis [25] Face Region -Edges Visible Wang [7] Face Region X FDA Descriptor Visible Song [38] None -Binary Edge Images Visible Shafi [14] Face Region -Hybrid Visible Khairosfaizal [10] Face Region -Circular Hough Transform Visible Dowdall [19] None -Integral Projections NIR Zhu [20] None X Kalman Filtering NIR Previous Work [9] Face [14] combine the work of [33], [34], and [37] to create a hybrid eye detection algorithm utilizing illumination, color, and edge-density information to determine the location of the eyes. Lu et al [17] uses rectangular as well as pixel-pattern-based texture features while Khairosfaizal et al [10] uses circular Hough transforms.…”

Section: Introductionmentioning

confidence: 99%

Accurate eye localization in the Short Waved Infrared Spectrum through summation range filters

Whitelam

Bourlai

2015

Computer Vision and Image Understanding

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The majority of facial recognition systems depend on the correct location of both the left and right eye centers in an effort to geometrically normalize face images. We propose a novel eye detection algorithm that efficiently locates the eye centers in five different bands of the SWIR spectrum, ranging from 1150nm up to 1550nm in increments of 100nm. Our eye detection methodology utilizes a combination of algorithmic steps, including 2D normalized correlation coefficients as well as summation range filters to effectively find the eyes in the aforementioned SWIR wavelengths. We validate our method by comparing our approach with currently available eye detection algorithms including a commercial face recognition software in which one of its capabilities is the extraction of the eye locations and a state of the art academic approach. Eye detection results as well as face recognition studies show that our proposed approach outperforms all other approaches, including the state of the art (originally designed to work in the visible band), when operating in the SWIR spectrum. We also show that our approach is robust to typical image degradation factors including spatial resolution changes, image compression, and image blurring. This is an important achievement that has also practical value for biometric operators. It is impractical to manually annotate thousands to millions of eye centers, therefore, a quick and robust method for automatically determining the eye center locations is needed.

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Eyes extraction from facial images using edge density

Cited by 7 publications

References 6 publications

Human visual system inspired object detection and recognition

Human visual system inspired object detection and recognition

On designing an unconstrained tri-band pupil detection system for human identification

Accurate eye localization in the Short Waved Infrared Spectrum through summation range filters

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