In this article, an expression recognition algorithm based on feature fusion was proposed. First, 40 sets of Gabor filters were selected to perform filtering operations on the expression images to enhance the texture features of the expression images, and subsequently, Local Binary Patterns(LBP) operators were used to perform feature extraction on the filtered images output by each Gabor channel to obtain LBP feature maps. Then these characteristic graphs are taken as the input of the convolutional neural network and the convolutional neural network is trained.Finally, the input of the fully connected layer of the trained convolutional neural network was taken out separately as the features of the expression image, and these features are classified and identified using the extreme learning machine algorithm. The experimental results showed that the method in this paper was better than the method using a single feature and can effectively improve the recognition rate in expression recognition.
In this paper, An improved algorithm for the extreme learning machine is proposed and applied to SAR target recognition.In order to solve the influence of the noise and spatial distribution of the training samples on the calculation of the classification plane, different penalty factors are given to different training samples, and according to this, the “weighted extreme learning machine” is proposed. And then,the kernel function is introduced into the "extreme learning machine" to improve the ability of nonlinear function approximation. Considering that the general training algorithm of the weighted extreme learning machine is slow and consumes a lot of computer memory when the number of training samples is large, a training method based on conjugate gradient algorithm is proposed. The test on "banana benchmark data" shows that the weighted extreme learning machine based on the conjugate gradient method can complete the convergence in the number of iterations far less than the number of samples, and the calculation speed is much faster than the traditional algorithm. Finally, this proposed algorithm is applied to SAR target recognition. The test on MSTAR data set shows that the proposed algorithm is not only extremely fast in SAR target recognition, but also has better recognition performance than support vector machine, general limit learning machine, BP neural network and other algorithms.
This paper proposed a face recognition algorithm based on conjugate gradient extreme learning machine. General extreme learning machine algorithm, which is gained by using method of calculating generalized inverse, the process is a large amount of computation and memory consumption. For this problem, this paper proves the positive definiteness of the calculated matrix, and based on this, an extreme learning machine solution algorithm based on conjugate gradient algorithm was proposed and kernel function is introduced to improve its nonlinear classification performance. At the same time, DAG method is used to extend the binary classification conjugate gradient extreme learning machine to multi-classification problems. Experimental results show that the computational speed of the algorithm in this paper is faster than that of the general extreme learning machine algorithm, and the classification accuracy is higher than that of the general extreme learning machine algorithm.