Method for identifying faces in videos based on incremental learning of face partitioning visual representations

Abstract

The invention provides a method for identifying faces in videos based on incremental learning of face partitioning visual representations and belongs to the field of pattern recognition. According to the method, an Adaboost algorithm is used for detecting frontal face images in a first frame of the face videos, a Camshift algorithm is used for tracking, all face images are obtained, in the process of reading the face images in the videos, incremental cluttering is carried out on the face images, and a representative image is selected from each kind of face images; the representative images are processed, and a visual dictionary based on the piece visual representations is learnt; the visual dictionary is used for carrying out the representations on the face images; finally, according to similar matrices, the videos composed of the face images are identified. According to the method, an identification rate and robustness of the video faces can be improved under the state that illumination, postures and tracking results are not ideal. The faces in the videos can be detected, tracked and identified effectively, conveniently and automatically.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to technologies such as image processing and computer vision, in particular to a face recognition method based on incremental learning of face block visual representation. Background technique [0002] Face recognition in video is mainly for the analysis and processing of moving image sequences containing people. The face recognition problem can be defined as: input (query) still images or videos in the scene, and use the face database to identify or verify one of the scenes. one or more people. Face recognition based on still images usually refers to inputting (querying) a still image and using a face database to identify or verify the face in the image. Video-based face recognition refers to inputting (querying) a video and using a face database to identify or verify the face in the video. [0003] Category is a basic attribute of all things in the world. Things of the same cate...

AI Technical Summary

Problems solved by technology

At present, many algorithms have been studied on face recognition (classification) in video scenes, but they often have certain shortcomings. For example, the collection and labeling of the database is required, the training samples need to be retrained, and incremental updates cannot be performed.

In addition, due to the large degree of change in the face posture in the video and the influence of external factors such as lighting, some recognition (classification) algorithms can achieve good performance under certain conditions, but they often fail under complex environmental conditions. Unable to achieve good recognition results, detection and tracking results are not ideal

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