The dominant challenge of vital signal monitoring based on facial video is to eliminate the interference of motion artifacts. In this paper, we propose a non-contact heart rate detection method based on an adaptive filter constructed by head movement information to tackle the noise of motion artifacts caused by the rigid rotation of the subject's head. The two-dimensional and three-dimensional feature points of the subject’s face are used to calculate the yaw and pitch Euler angles of the head movement, then the yaw and pitch Euler angles are used as a novel signal quality index (SQI) for modulating process noise covariance to construct an adaptive Kalman filter, and finally robust heart rate is estimated by this method. The experimental results show that the proposed method can effectively suppress the noise caused by the head rigid rotation with an average absolute error of 2.22 beat/min and a root mean square error of 2.76 beat/min, which are statistically significant with an accuracy improvement of 9% and 24.6%, respectively, compared with the existing methods. The adaptive non-contact robust heart rate detection technique based on head rigid rotation may effectively enhance the accuracy in real-world motion situations, as well as broaden the range of applications for IPPG in the field of the video-based monitoring of health conditions.