Wang Xing

TL;DR: In this paper, a three-dimensional face recognition method and system is presented, where image data of a to-be-identified target face are collected and a face key point is calibrated.

TL;DR: In this article, a gesture recognition method based on a depth sensor is presented, which comprises the steps of sequentially obtaining depth flow information and human skeleton node data, cutting a gesture region, performing positive side and binaryzation are performed on 3D point cloud coordinates of the gesture region; gesture feature extraction, normalization and dimension reduction treatment are performed; gesture features are screened to obtain an optimal gesture feature subset; a support vector machine gesture training classifier is adopted, and a classification result is obtained; filtering is performed on the gesture classification result.

TL;DR: In this paper, a face characteristic point detection method and device and equipment is provided. But the method is not suitable for the detection of face characteristic points in the 3D face model.

TL;DR: In this paper, the authors proposed an audio signal correction method based on pre-determined sample difference parameters, determining difference parameters between common audio signals and reference audio signals, and correcting the common audio signal to obtain corrected audio signals matched with the reference audio signal.