Facial recognition: an enabling technology for augmented cognition applications
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
Robust face recognition using advanced correlation filters with bijective-mapping preprocessing
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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We propose a motion-compensated video coding system employing dense motion fields. The dense motion field is calculated at the transmitter, and the motion information is efficiently encoded and transmitted along with the residual frame. The motion estimation is performed by existing techniques in the literature, while we focus on the coding of the motion field and the displaced frame difference (DFD) frame. The dense motion field formulation leads to several novel and distinct advantages. The motion field is encoded in a lossy manner to make the motion rate manageable. The more accurate and precise motion description allows us to predict where the DFD energy will be significant, thus leading to a more efficient DFD encoder compared to applying traditional still-image coding techniques. Furthermore, the dense motion field framework allows us to refine and tailor the motion estimation process such that the resulting DFD frame is easier to encode. Simulations demonstrate superior performance against standard block-based coders, with greater advantages for sequences with more complex motion