A reliable method for the detection and tracking of facial landmarks in image sequences is presented. Given a set of prespecified facial landmarks in a reference face image, a bank of composite matched filters is constructed for reliable detection and accurate location of the landmarks in an input image sequence. The filter bank is dynamically adapted to each captured frame by learning from current and past landmark detections and considering geometrical modifications of the landmarks. The detected landmarks are accurately tracked using a kinematic motion model that predicts their coordinates in future frames. The performance of facial landmark detection and tracking obtained with the proposed method is tested by processing real-life image sequences. The obtained results are analyzed and discussed in terms of objective measures.
An algorithm for facial landmark detection based on template matched filtering is presented. The algorithm is able to detect and estimate the position of a set of prespecified landmarks by employing a bank of linear filters. Each filter in the bank is trained to detect a single landmark that is located in a small region of the input face image. The filter bank is implemented in parallel on a graphics processing unit to perform facial landmark detection in real-time. Computer simulation results obtained with the proposed algorithm are presented and discussed in terms of detection rate, accuracy of landmark location estimation, and real-time efficiency.
A real-time tracking system based on adaptive correlation filtering and state prediction is proposed. The system is able to
estimate at high-rate the position of multiple targets within the observed scene by taking into account information of past
and present scene-frames. The position of the targets in the current frame is estimated with the help of a bank of
composite correlation filters applied to several small regions taken from the observed scene. These small regions are
updated in each frame according to information from a state predictor based on the motion model of targets in a twodimensional
plane. The proposed system is implemented on a graphics processing unit to take advantage of massive
parallelism. Computer simulation results obtained with the proposed system are presented and discussed in terms of
tracking accuracy and real-time operation efficiency.
A real-time system for multiclass object recognition is proposed. The system is able to identify and correctly
classify several moving targets from an input scene by using a bank of adaptive correlation filters with complex
constraints implemented on a graphics processing unit. The bank of filters is synthesized with the help of
an iterative algorithm based on complex synthetic discriminant functions. At each iteration, the algorithm
optimizes the discrimination capability of each filter in the bank by using all available information about the
known patterns to be recognized and unwanted patterns to be rejected such as false objects or a background.
Computer simulation results obtained with the proposed system in real and synthetic scenes are presented and
discussed in terms of pattern recognition performance and real-time operation speed.