Abstract: Embodiments of this application disclose a focusing method and an electronic device, including: In response to a first operation, an electronic device starts photographing, and displays a first interface. The electronic device displays a Kth frame of preview image in the first interface at a first moment. The Kth frame of preview image is collected by the camera by using a face of a first target as a first focusing frame, and the face of the first target meets a preset facial recognition condition in the Kth frame of preview image. The electronic device displays an Mth frame of preview image in the first interface at a second moment. The Mth frame of preview image is obtained by the camera by focusing on the first focusing frame, the face of the first target does not meet the preset facial recognition condition in the Mth frame of preview image.

Abstract: In a method for facial feature point tracking, facial feature points of a face in a previous image of a to-be-tracked image are acquired in an image sequence. Facial feature point errors between the to-be-tracked image and the previous image are determined based on a preset error model and pixels in the to-be-tracked image. The facial feature point errors indicate differences between first coordinates of facial feature points in the to-be-tracked image, and second coordinates of facial feature points at corresponding positions in the previous image. The preset error model is trained based on facial feature points in a plurality of pairs of adjacent reference images. Further, the facial feature points of the face in the to-be-tracked image are determined based on the facial feature points of the face in the previous image and the facial feature point errors between the to-be-tracked image and the previous image.

Abstract: In a method for facial feature point tracking, facial feature points of a face in a previous image of a to-be-tracked image are acquired in an image sequence. Facial feature point errors between the to-be-tracked image and the previous image are determined based on a preset error model and pixels in the to-be-tracked image. The facial feature point errors indicate differences between first coordinates of facial feature points in the to-be-tracked image, and second coordinates of facial feature points at corresponding positions in the previous image. The preset error model is trained based on facial feature points in a plurality of pairs of adjacent reference images. Further, the facial feature points of the face in the to-be-tracked image are determined based on the facial feature points of the face in the previous image and the facial feature point errors between the to-be-tracked image and the previous image.