# Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation  
[PERSON]  
111 School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China; [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (X.W. ); [EMAIL_ADDRESS] (W.Z.) 2  
[PERSON] [PERSON] Zhuang School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China; [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (X.W. ); [EMAIL_ADDRESS] (W.Z.) 2  
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been efficient in exploring descriptive representations in natural images, such as autoencoders [11], Convolutional Neural Networks (CNNs) [12], and so on. Among these methods, [PERSON] et al. proposed group-aware deep feature learning (GA-DFL) to estimate ages with facial images [13]. Different from most previous methods using hand-crafted features for facial image description, GA-DFL uses a deep CNN framework to compute a discriminative feature descriptor per image automatically from raw pixels of facial images. Although a large number of feature descriptors have been proposed, most of them can only describe a part of the information inherent in images. Therefore, researchers look into representing images with multiple features. Traditional methods make use of multiple features by directly concatenating them, which is oversimplified. To solve this problem, researchers also apply manifold learning to combine different types of features [14; 15]. On the other hand, feature mapping tries to learn the mapping relationship from face images to age labels. With descriptive representations of facial images, age estimation is usually considered as a regression or classification problem [5; 16]. Linear regression and twin Gaussian processes are also used for pose estimation [17; 18]. [PERSON] et al. proposed conducting age estimation by taking both ordinality and locality into consideration [19]. Previous approaches made an over-simplified assumption that the mapping from images into poses is linear. To tackle this nonlinear issue, methods based on deep learning have been applied. They can train a series of nonlinear mapping models [20; 21; 22; 23; 24]. However, these models cannot explicitly define the ordinal relationship between facial images and chronological ages, because they usually suffer from insufficient and unbalanced training data. In this way, they still cannot be used in practical scenarios. Although many methods for age estimation with images have been proposed, they usually use only a single type of feature. Even with popular neural networks, they apply only a single structure of neural networks, which still suffers the so-called \"semantic gap\". Currently, multiple types of features have been used in many applications. Inspired by it, we proposed a Multiple Network Fusion with Low-Rank Representation (MNF-LRR) for the age estimation method. The contributions of this paper can be summarized by the following:  
* The first and key contribution is a novel framework that estimates ages with a single image by fusing multiple deep neural networks. This framework is flexible and the hidden representations are computed independently. In this way, different types of neural networks, different network structures, and different features can be used in this framework. * The second contribution of the proposed method is multiple-network fusion with low-rank learning. Low-rank representation is naturally sparse. Besides, different types of features are extracted by different networks and their distributions can be observed clearly. To improve traditional low-rank learning, we introduce a hypergraph manifold. In this way, samples can be represented in a unified low-rank space and the process of fusion can be achieved in this space. * The third contribution is that the performance of the proposed method is verified on datasets from the Internet Movie Database (IMDB) and Wikipedia (WIKI).