Frontal face localization using linear discriminant

In this paper, we present a novel approach for frontal face detection in gray-scale images. We represent both faces and clutter by using two-dimensional wavelet decomposition. To characterize the statistical dependency between different levels of wavelet, we introduce a Hidden Markov Model (HMM), in which a number of discrete states at each level capture the diversity of faces as well as clutter. Our experiments indicate that the proposed algorithm outperforms conventional templatebased methods such as matched filter and eigenface methods.

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Tracking the optic nerve head in OCT video using dual eigenspaces and an adaptive vascular distribution model

Koozekanani

Boyer

Roberts

et al.

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001

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Optical coherence tomography (OCT) is a new ophthalmic imaging modality generating cross sectional views of the retina. OCT systems form images in 1.5 seconds by directing a superluminescent diode (SLD) beam over the retinal surface. Involuntary ocular motion may occur, however, causing incorrect locations to be imaged. This motion may leave no obvious artifacts and thus go undetected. For glaucoma monitoring especially, knowing the measurement location is crucial. The commercially available OCT system displays a near-IR video of the SLD beam traversing the retinal around the optic nerve head. We developed a prototype system to detect the nerve head and SLD beam position in this video, and report the actual scan path relative to the nerve head. This system must cope with low image contrast and few reliable retinal features. In its adaptive model generation phase, the system directly detects vasculature and the nerve head and builds an individual model of the vascular pattern. The nerve head identification is multitiered, using a novel, dual-eigenspace technique and a geometric comparison of detected vessel positions and nerve head hypotheses. In its operational phase, a correspondence is achieved between detected vasculature and the model. The system was evaluated on video of three subjects not used to form the eigenspaces. The system located the optic nerve head to within 5 pixels in 99 % of 2800 video fields manually inspected, and was thus able to determine the true scan path relative to the nerve head.

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Frontal face localization using linear discriminant

Cited by 6 publications

References 7 publications

N-mean kernel filter and normalized correlation for face localization

N-mean kernel filter and normalized correlation for face localization

An image-based Bayesian framework for face detection

Tracking the optic nerve head in OCT video using dual eigenspaces and an adaptive vascular distribution model

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