Real-Time Iris Detection on Coronal-Axis-Rotated Faces

Perez, Claudio A.; Lazcano, Vanel; Estévez, P. A.

doi:10.1109/tsmcc.2007.900647

Cited by 32 publications

(23 citation statements)

References 31 publications

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“…The proposed method for iris and face detection in rotated faces is based on a method previously developed by Maio-Maltoni for frontal face detection (Maio and Maltoni 2002) and is an extension to our previous method for coronal rotation in (Perez et al 2001;Perez et al 2007). The method is built into three stages requiring only grey level information without background restrictions.…”

Section: Methodsmentioning

confidence: 99%

“…In (Perez et al 2007) the method was extended to faces with coronal rotations but with no transversal rotations. In this article we present an extension to our previous method for face detection (Perez et al 2001;Perez et al 2007) to include wide coronal (À40 , þ40 ) and transversal (À45 , þ45 ) axis rotations in real-time for iris detection. The method uses templates derived from anthropometric data about faces and eyes to guide the face and eye detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Three different templates are built to allow face detection. The coarse face detection stage uses the face detection approach previously proposed in (Perez et al 2007) (Martinez and Benavente 1998). The ratio between two line integrals over a semicircular template is computed to detect the iris-sclera boundary.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Template Based Face and Iris Detection on Rotated Faces

Perez

Lazcano

Estévez

et al. 2009

International Journal of Optomechatronics

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Real-time iris and face detection on video sequences is important in applications such as study of the eye function, drowsiness detection, man-machine interfaces, face recognition security and multimedia retrieval. In this work, we present a real-time template based method for iris detection in faces with wide coronal (240 , 140 ) and transversal (245 , 145 ) axis rotations. This method is based on anthropometric templates that were constructed off-line for face coronal and transversal rotation, using face features such as elliptical shape, location of the eyebrows, nose and lips. A line integral is computed using these templates over the fine directional image to find the actual face location, face size and rotation angle. This information provides a region to search for the eyes and the iris boundary is detected. Results computed on five video sequences including coronal and transversal rotations with over 1,700 frames show correct face detection rate of 98.5% and iris detection rate of 94.4%. The method was compared with a ''weighting mask method'' on two video sequences showing an improved performance. The method was also compared for eye detection to a method using combined binary edge and intensity information in two subsets of the AR face database (63 and 564 images). Different disparity errors were considered and for the smallest error, a 100% correct detection was reached in the AR-63 subset and 99.8% was obtained in the AR-564 subset.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Template Based Face and Iris Detection on Rotated Faces

Perez

Lazcano

Estévez

et al. 2009

International Journal of Optomechatronics

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“…Face recognition in real time is a topic of active research, and several methods have been proposed to perform this task [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models

Cament

Galdames

Bowyer

et al. 2015

Pattern Recognition

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“…[1][2][3]. Facial images are dramatically changed by lighting variations and therefore may cause performance degradation both in face detection and in recognition [4].…”

mentioning

confidence: 99%

Genetic optimisation of illumination compensation methods in cascade for face recognition

et al. 2010

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Face detection and recognition depend strongly on illumination conditions. In this reported work, genetic algorithms are used to optimise parameters of the modified local normalisation and self-quotient image methods in cascade for illumination compensation to improve face recognition. The main novelty of the proposed method is that it applies to non-homogeneous as well as homogeneous illumination conditions. The results are compared to those of the best illumination compensation methods published in the literature, obtaining 100% recognition on faces with non-homogeneous illumination and significantly better results than other methods with homogeneous illumination.

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Real-Time Iris Detection on Coronal-Axis-Rotated Faces

Cited by 32 publications

References 31 publications

Real-Time Template Based Face and Iris Detection on Rotated Faces

Real-Time Template Based Face and Iris Detection on Rotated Faces

Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models

Genetic optimisation of illumination compensation methods in cascade for face recognition

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