Tracking and Recognizing Multiple Faces Using Kalman Filter and ModularPCA

Real-time tracking and recognizing multiple faces in complex environments has the ability to provide e_cient security automation to large areas. Previous research has shown that Kalman filter techniques paired with the traditional face detection methods can be used to track one or more faces in a viewing region, but prove unreliable under variant conditions due to the inability to reliably distinguish between multiple trackers. A real-time face tracking and recognition system is presented that is capable of processing multiple faces simultaneously. The proposed system utilizes the Kalman filter for tracking and uses a low-level recognition system to properly distinguish between the many trackers. This low-level system is created using a face database of twenty unrelated people trained using Modular Principal Component Analysis (MPCA) and classification is performed using a feature correlation metric. After tracking the faces, they are then analyzed by a high-level face recognition subspace which is created using a large database of people and processed using Adaptive MPCA. The overall system is shown to provide reliable tracking of more than one person and to allow a more accurate recognition rate due to the ability to create a time-average of the recognized faces.