This paper considers the face identification task in video sequences where the individual’s face presents variations;
such as expressions, pose, scale, shadow/lighting and occlusion. The principles of Synthetic Discriminant
Functions (SDF) and K-Law filters are used to design an adaptive unconstrained correlation filter (AUNCF). We
developed a face tracking algorithm which together with a face recognition algorithm were carefully integrated
into a video-based face identification method. First, a manually selected face in the first video frame is identified.
Then, in order to build an initial correlation filter, the selected face is distorted so that it generates a training set.
Finally, the face tracking task is performed using the initial correlation filter which is updated through the video
sequence. The efficiency of the proposed method is shown by experiments on video sequences, where different
facial variations are presented. The proposed method correctly identifies and tracks the face under observation
on the tested video sequences.
Correlation filters have become an important tool for detection, localization, recognition and object tracking in digital
media. This interest in correlation filters has increased thanks to the processing speed advances of the computers that
enable the implementation of digital correlation filters in real-time. This paper compares the performance of three
correlation filters in the activity of object recognition, specifically human faces with variations in facial expression, pose,
rotation, partial occlusion, illumination and additive white Gaussian noise. The analyzed filters are k-law, MACE and
OTSDF. Simulation results show that the k-law nonlinear composite filter has the best performance in terms of accuracy
and false acceptance rate. Finally, we conclude that a preprocessing algorithm improves significantly the performance of
correlation filters for recognizing objects when they have variations in illumination and noise.
During a cognitive stimulation session where elders with cognitive decline perform stimulation activities, such as
solving puzzles, we observed that they require constant supervision and support from their caregivers, and caregivers
must be able to monitor the stimulation activity of more than one patient at a time. In this paper, aiming at providing
support for the caregiver, we developed a vision-based system using an Phase-SDF filter that generates a composite
reference image which is correlated to a captured wooden-puzzle image. The output correlation value allows to
automatically verify the progress on the puzzle solving task, and to assess its completeness and correctness.