PROJECT TITLE :

From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild

ABSTRACT:

We propose a face alignment framework that depends on the texture model generated by the responses of discriminatively trained half-primarily based filters. Unlike standard texture models designed from pixel intensities or responses generated by generic filters (e.g. Gabor), our framework has 2 important benefits. Initial, by virtue of discriminative training, invariance to external variations (like identity, cause, illumination and expression) is achieved. Second, we show that the responses generated by discriminatively trained filters (or patch-consultants) are sparse and will be modeled using a terribly small number of parameters. Hence, the optimization strategies based mostly on the proposed texture model will better cope with unseen variations. We tend to illustrate now by formulating each part-based mostly and holistic approaches for generic face alignment and show that our framework outperforms the state-of-the-art on multiple ”wild” databases. The code and dataset annotations are available for analysis purposes from http://ibug.doc.ic.ac.uk/resources.


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