Fast Face Location in Complex Backgrounds

Maio, Dario; Maltoni, Davide

doi:10.1007/978-3-642-72201-1_36

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…Maio and Maltoni (1998) use a generalised Hough transform to locate image ellipses which approximate to the boundaries of faces. Wang et al (2003) fit an ellipse to the image of the outer boundary of the iris in the human eye.…”

Section: Applicationsmentioning

confidence: 99%

Application of the Fisher-Rao Metric to Ellipse Detection

Maybank

2006

Int J Comput Vision

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Abstract. The parameter space for the ellipses in a two dimensional image is a five dimensional manifold, where each point of the manifold corresponds to an ellipse in the image. The parameter space becomes a Riemannian manifold under a Fisher-Rao metric, which is derived from a Gaussian model for the blurring of ellipses in the image. Two points in the parameter space are close together under the Fisher-Rao metric if the corresponding ellipses are close together in the image. The Fisher-Rao metric is accurately approximated by a simpler metric under the assumption that the blurring is small compared with the sizes of the ellipses under consideration. It is shown that the parameter space for the ellipses in the image has a finite volume under the approximation to the Fisher-Rao metric. As a consequence the parameter space can be replaced, for the purpose of ellipse detection, by a finite set of points sampled from it. An efficient algorithm for sampling the parameter space is described. The algorithm uses the fact that the approximating metric is flat, and therefore locally Euclidean, on each three dimensional family of ellipses with a fixed orientation and a fixed eccentricity. Once the sample points have been obtained, ellipses are detected in a given image by checking each sample point in turn to see if the corresponding ellipse is supported by the nearby image pixel values. The resulting algorithm for ellipse detection is implemented. A multiresolution version of the algorithm is also implemented. The experimental results suggest that ellipses can be reliably detected in a given low resolution image and that the number of false detections can be reduced using the multiresolution algorithm.

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Section: Applicationsmentioning

confidence: 99%

Application of the Fisher-Rao Metric to Ellipse Detection

Maybank

2006

Int J Comput Vision

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“…In our preliminary work [47] the "ne location and face veri"cation sub-tasks were performed sequentially. From the experimental evidences we argued that better results could be achieved by executing both operations simultaneously.…”

Section: Flfv -5ne Location and Face Veri5cationmentioning

confidence: 99%

Real-time face location on gray-scale static images

Maio

Maltoni

2000

Pattern Recognition

Self Cite

109

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“…One of the simplest and fastest methods to realize the feature extraction is the projection of the image or the edge image on the vertical axis to find the eyes or the mouth and on the horizontal axis to locate the nose [22], [7], [19]. Several other methods are currently used to perform the feature extraction: Gabor filter [25], oval detection [31], [24], etc. A similarity measurement between features is then used for face recognition or face detection task: Mahalanobis distance [7], crosscorrelation [2], [7], [5], graph matching [25], elastic matching of features [40], decision tree [19], neural network [7], belief network [8]...…”

Section: Introductionmentioning

confidence: 99%

A fast and accurate face detector based on neural networks

Feraund

Bernier

Viallet

et al. 2001

IEEE Trans. Pattern Anal. Machine Intell.

235

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ÐDetecting faces in images with complex backgrounds is a difficult task. Our approach, which obtains state of the art results, is based on a new neural network model: the Constrained Generative Model (CGM). Generative, since the goal of the learning process is to evaluate the probability that the model has generated the input data, and constrained since some counterexamples are used to increase the quality of the estimation performed by the model. To detect side view faces and to decrease the number of false alarms, a conditional mixture of networks is used. To decrease the computational time cost, a fast search algorithm is proposed. The level of performance reached, in terms of detection accuracy and processing time, allows to apply this detector to a real world application: the indexation of images and videos.

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Fast Face Location in Complex Backgrounds

Cited by 11 publications

References 24 publications

Application of the Fisher-Rao Metric to Ellipse Detection

Application of the Fisher-Rao Metric to Ellipse Detection

Real-time face location on gray-scale static images

A fast and accurate face detector based on neural networks

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