Face recognition method of deep convolutional neural network

Abstract

The invention discloses a face recognition method of a deep convolutional neural network, which reduces the time complexity, and enables a weight in the network to still have a high classification capacity under the condition of reducing the number of training samples. The face recognition method comprises a training stage and a classification stage. The training stage comprises the steps of (1) randomly generating a weight wj between an input unit and a hidden unit and an offset bj of the hidden unit, wherein j equals to 1,...,L and represents the number of the weight and the offset, and the total number is L; (2) inputting a training image Y and a label thereof, by using a forward conduction formula hw, b(x)=f(W<T>x), wherein hw, b(x) is an output value, x is input, and an output value hw, b(x<(i)>) of each layer is calculated; (3) calculating the offset of the last layer according to a label value and an output value of the last layer; (4) calculating the offset of each layer according to the offset of the last layer, and acquiring the gradient direction; and (5) updating the weight. The classification stage comprises the steps of (a) keeping all parameters in the network to be unchanged, and recording a category vector outputted by the network of each training sample; (b) calculating a residual error delta, wherein delta=||hw, b(x<(i)>)-y<(i)>||<2>; and (c) classifying a tested image according to the minimum residual error.

Description

Technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and specifically relates to a face recognition method of a deep convolutional neural network. Background technique [0002] Feature extraction has always been one of the difficulties in the field of pattern recognition. Traditional feature-based recognition methods all define a feature in advance, and then perform classification and recognition based on the defined feature. As the development of traditional machine learning, deep learning has been widely used in many fields because it can automatically learn more suitable representation features layer by layer. The general deep learning algorithm will lose the structural information of the original image when performing image recognition, thus affecting the recognition effect. As one of the methods of deep learning, convolutional neural network, on the premise of inheriting deep learning to automatically learn and ext...

AI Technical Summary

Problems solved by technology

However, through experiments, it is found that when the training samples are reduced, the recognition rate of the algorithm for the test samples will drop sharply; especially when the samples used for training are less than 20,000, the recognition rate is only 59.21%.

This deep neural network learning model requires a large number of labeled samples for training, and the time complexity is extremely high. It often requires tens of thousands of iterative updates to obtain better recognition performance.

Then in practical applications, the cost of sample labeling is very high, and the time complexity requirements are also very demanding (for example, real-time recognition is sometimes required)

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