LBP and deep learning-based human face identification method

Abstract

The invention relates to a #-shaped LBP and deep learning combination-based human face identification method, and belongs to the technical field of artificial intelligence. For the problems of insufficient feature extraction and classifier fitting of a conventional LBP, a human face identification method based on a #-shaped LBP and deep learning of a local texture feature is proposed. By utilizingan improved LBP algorithm, the local texture feature of a human face image is extracted, and an LBP histogram is established; then a deep belief network-based deep learning framework is established,the LBP histogram is input to a deep belief network, the network is trained by adopting an unsupervised layer-by-layer training method and a supervised BP algorithm, self-learning and self-optimization of the network are realized, and network parameters are obtained; and finally, the human face image is classified and identified by utilizing the DBN. According to the method, the distinctive texture feature of the human face image can be extracted; the superiority of the algorithm in identification rate is verified; and the robustness is relatively strong.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular, relates to a face recognition method based on LBP and deep learning. Background technique [0002] Face recognition is an identification technology based on facial features, which has attracted great attention from various fields. In recent years, methods based on deep learning ideas have been greatly developed and applied, and breakthrough results have been achieved in research fields such as computer vision and object recognition, providing algorithmic support for the development of computer science in the direction of intelligence. At present, the method of deep learning has been introduced into the related research field of face recognition by more and more researchers, and many satisfactory results have been achieved. In 2006, Hinton proposed the DBN algorithm, which is a representative deep learning method. Its advantage is that it has a strong abilit...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of insufficient feature extraction of traditional local binary patterns (LBP) and classifier fitting, the present invention provides a face recognition method based on LBP and deep learning that is beneficial to extract more useful texture information of face images

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