Content area
Full text
Received Jul 4, 2017; Revised Nov 20, 2017; Accepted Dec 4, 2017
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Although many sophisticated algorithms have been proposed, face recognition is still a challenging problem affected by many external factors such as the occlusion, illumination, noise, geometric distortion, translation, and rotation of face images. Recently, face recognition algorithms based on deep learning have achieved good performance [1–6]. Stacked autoencoder (SAE) [7] is an unsupervised neural network approach, where the input and target values are the same. In SAE, the deepest hidden layer carries the features we are interested in. The input layer and the deepest hidden layer are connected by multiple encoding layers, and the deepest hidden layer and output layer are connected by multiple decoding layers. The activation values of the deepest hidden layer nodes are essentially the deep representation features which are used to perform classification tasks by feeding them to the corresponding classifier such as Softmax. In order to obtain more robust features, random noise can be added to the input layer of SAE. This method is called Stacked Denoising Autoencoders (SDAE) [8]. In practical applications, the values of the input layer nodes can be set to be 0 with a certain probability and it can extract more robust features. However, SAE and SDAE both adopt the fully connected way to establish a connection between the input layer (hidden layer) and another hidden layer. The disadvantage is that a large number of parameters need to be learned when training SAE and SDAE. Take SDAE as an example, we set the hidden nodes to be 100, and the number of the network weights and bias will be