Classifying variable objects using a flexible shape model

Lanitis, Andreas; Cristina, Taylor,; Ahmed, Tanzia; Cootes, TF

doi:10.1049/cp:19950622

Cited by 2 publications

(1 citation statement)

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“…Since Mahalanobis distance uses covariance information, it has the ability to suppress the effect of parameters responsible for within-class variation. We have described elsewhere [26] how the interclass variation parameters can be explicitly isolated, using canonical discriminant analysis. When a new face image is presented the shape model is fitted and the appearance parameters computed.…”

Section: Methodsmentioning

confidence: 99%

Automatic interpretation and coding of face images using flexible models

Lanitis

Taylor

Cootes

1997

IEEE Trans. Pattern Anal. Machine Intell.

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540

239

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Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression , and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance , and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located , and a set of shape , and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition , and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting , and facial expression. The system performs well on all the tasks listed above.

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Section: Methodsmentioning

confidence: 99%