  To dynamically detect the facial landmarks in the video, we propose a novel
hybrid framework termed as detection-tracking-detection (DTD). First, the face
bounding box is achieved from the first frame of the video sequence based on a
traditional face detection method. Then, a landmark detector detects the facial
landmarks, which is based on a cascaded deep convolution neural network (DCNN).
Next, the face bounding box in the current frame is estimated and validated
after the facial landmarks in the previous frame are tracked based on the
median flow. Finally, the facial landmarks in the current frame are exactly
detected from the validated face bounding box via the landmark detector.
Experimental results indicate that the proposed framework can detect the facial
landmarks in the video sequence more effectively and with lower consuming time
compared to the frame-by-frame method via the DCNN.
