Twin convolutional neural network face recognition algorithm introducing a perception model

Abstract

The invention discloses a twin convolutional neural network face recognition algorithm introducing a perception model. Firstly, a twin convolutional neural network is introduced as an overall networkstructure model, so that external interference can be effectively reduced, over-fitting is avoided; a sensing model is added to a twin convolutional neural network structure on the basis, the networkwidth is increased, the effect of information cross-channel connection is achieved, the adaptability of the network to the scale is improved, and meanwhile richer feature extraction can be achieved bymeans of the advantage of hardware dense matrix optimization. The whole training process is assisted by a loop learning rate strategy optimization algorithm, so that the optimal learning rate is easyto find, the model convergence can be accelerated, the network performance is improved, and high-precision face recognition under a non-limiting condition is effectively realized. The algorithm is simple in structure, has high robustness for face recognition under the non-limiting condition, can improve the training speed and improve the face recognition accuracy, and is suitable for small-scaledata sets.

Description

technical field

[0001] The invention belongs to the technical field of image recognition, and in particular relates to a twin convolutional neural network face recognition algorithm that introduces a perceptual model. Background technique

[0002] The improvement of security awareness has prompted people's demand for public and personal security to continue to rise. How to accurately and quickly identify personal identities and protect information security has become a key social problem that needs to be solved urgently. Therefore, a variety of biometric identification technologies have emerged as the times require, and face recognition technology has attracted much attention due to its advantages of convenience, speed, and non-invasiveness, and its research results are abundant. Summarizing the classic face recognition algorithm, it can be found that principal component analysis (PCA) reduces the dimensionality of the original data feature space through matrix transformatio...

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