一种卷积神经网络和极限学习机相结合的人脸识别方法

卷积神经网络是一种很好的特征提取器，但却不是最佳的分类器，而极限学习机能够很好地进行分类，却不能学习复杂的特征，根据这两者的优点和缺点，将它们结合起来，提出一种新的人脸识别方法。卷积神经网络提取人脸特征，极限学习机根据这些特征进行识别。本文还提出固定卷积神经网络的部分卷积核以减少训练参 数，从而提高识别精度的方法。在人脸库ORL和XM2VTS上进行测试的结果表明，本文的结合方法能有效提高人脸识别的识别率，而且固定部分卷积核的方式在训练样本少时具有优势。

Face Recognition Algorithm Based on Combination of Convolutional Neural Networks and Extreme Learning Machine

Convolutional neural networks are good at learning features, but not always optimal for classification, while extreme learning machines are good at producing decision surfaces from well-behaved feature vector, but cannot learn complicated invariances. Based on the advantages and disadvantages of convolutional neural networks and extreme learning machine, we present a hybrid system where a convolutional neural network is trained to extract features and an extreme learning machine is trained from the features learned by the convolutional neural networks to recognize faces. We also propose prefix part of the filters in the convolutional layers to reduce parameters for improving the recognition accuracy. The experimental results obtained on the ORL and XM2VTS databases show that the proposed method can effectively improve the performance of face recognition, and the method of prefixing part of the filters is better than the method of stochastic filters in small training data.