关键词: 人体身份识别, 脸部数据, 骨骼数据, 排序截断均值法, 匹配识别准确度

Abstract: In order to understand the features learning process, reduce storage of data and improve recognition accuracy, an improved KNN method is proposed for human identification based on Kinect v2’s facial data and skeletal data. First of all, 3-D position information of facial feature points and skeletal joints is extracted by Kinect v2 and then the characteristic information of strong understanding like eye width and arm length can be calculated. It is proposed that an improved truncated mean method can restrain singular values by sorting data and intercepting the intermediate data, and an improved KNN method based on the accuracy of matching recognition is applied to predict human identity. Experimental results show that the proposed clustering method has higher accuracy of matching recognition and the improved classification method improves the accuracy of recognition.

Key words: human identity recognition, facial data, skeletal data, sort truncated mean method, accuracy of matching recognition