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

Detecting facial landmarks in the video based on a hybrid framework