Abstract
Age estimation is applicable in various fields, and among them, research on age estimation using human facial images, which are the easiest to acquire, is being actively conducted. Since the emergence of deep learning, studies on age estimation using various types of convolutional neural networks (CNN) have been conducted, and they have resulted in good performances, as clear images with high illumination were typically used in these studies. However, human facial images are typically captured in low-light environments. Age information can be lost in facial images captured in low-illumination environments, where noise and blur generated by the camera in the captured image reduce the age estimation performance. No study has yet been conducted on age estimation using facial images captured under low light. In order to overcome this problem, this study proposes a new generative adversarial network for low-light age estimation (LAE-GAN), which compensates for the brightness of human facial images captured in low-light environments, and a CNN-based age estimation method in which compensated images are input. When the experiment was conducted using the MORPH, AFAD, and FG-NET databases—which are open databases—the proposed method exhibited more accurate age estimation performance and brightness compensation in low-light images compared to state-of-the-art methods.
Highlights
A human face contains biological information showing various attributes, such as identity, age, gender, emotions, and expressions
Age estimation has a wide range of applications in commercial areas, such as customer prediction and preference surveys according to age, security for controlling access based on age and statistical fields such as age surveys of an audience [6]
Human facial images acquired in low-illumination environments lose the information required for age estimation because various kinds of noise and blur are generated
Summary
A human face contains biological information showing various attributes, such as identity, age, gender, emotions, and expressions. Age estimation using human facial images entails several problems, including the uncontrollable, natural aging process, individual aging patterns, and large inter-class similarity and intra-class variation of subjects’ images within age classes [7]. For overcoming these drawbacks, image representation techniques such as the active appearance model (AAM) [8], the active shape model (ASM) [9], the aging pattern subspace model (AGES) [10], feature extraction techniques such as Gabor filters [11], linear discriminant analysis (LDA) [12], and local binary patterns (LBP) [13] have been used in the past. Since the emergence of deep learning, where feature extraction and learning are both involved in the process, using a convolutional neural network (CNN) has become popular in age estimation
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