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Facial landmarks detection systems using correlation filters known as Inner Product Detectors (IPDs) have been used often for the search of isolated patterns over face images. These systems use high level information as decision criteria for pattern classification, such as prior probability distributions.
In this dissertation, we suggest the use of spatial similarities among the facial landmarks for the joint determination of these patterns. Two methods have been developed and applied over a combination of the early local detection systems. This resulted in a complex system under global landmark detection paradigm.
The first method explores the symmetry patterns among the several patterns output by the local detectors for different landmarks. The other one explores graph matching for global landmark representation.