Abstract
Image-based age estimation is a challenging task since there are ambiguities between the apparent age of face images and the actual ages of people. Therefore, data-driven methods are popular. To improve data utilization and estimation performance, we propose an image-based age estimation method. Theoretically speaking, the key idea of the proposed method is to integrate multi-modal features of face images. In order to achieve it, we propose a multi-modal learning framework, which is called Multiple Network Fusion with Low-Rank Representation (MNF-LRR). In this process, different deep neural network (DNN) structures, such as autoencoders, Convolutional Neural Networks (CNNs), Recursive Neural Networks (RNNs), and so on, can be used to extract semantic information of facial images. The outputs of these neural networks are then represented in a low-rank feature space. In this way, feature fusion is obtained in this space, and robust multi-modal image features can be computed. An experimental evaluation is conducted on two challenging face datasets for image-based age estimation extracted from the Internet Move Database (IMDB) and Wikipedia (WIKI). The results show the effectiveness of the proposed MNF-LRR.
Highlights
Image-based age estimation tries to compute the age or age group with facial images
Facial images are often captured in wild conditions so they are influenced by large variations such as occlusion, lighting, shadow, and complex backgrounds
We propose a data-driven method for image-based age estimation
Summary
Image-based age estimation tries to compute the age or age group with facial images. It can be widely used in many applications such as biometric feature recognition, human–computer interaction (HCI), and so on. Sci. 2018, 8, 1601 been efficient in exploring descriptive representations in natural images, such as autoencoders [11], Convolutional Neural Networks (CNNs) [12], and so on Among these methods, Liu et al proposed group-aware deep feature learning (GA-DFL) to estimate ages with facial images [13]. Many methods for age estimation with images have been proposed, they usually use only a single type of feature. 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. The performance on this dataset indicates that the proposed MNF-LRR is suitable for practical and complicated applications
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