Abstract
Age estimation using facial images is applicable in various fields, such as age-targeted marketing, analysis of demand and preference for goods, skin care, remote medical service, and age statistics, for describing a specific place. However, if a low-resolution camera is used to capture the images, or facial images are obtained from the subjects standing afar, the resolution of the images is degraded. In such a case, information regarding wrinkles and the texture of the face are lost, and features that are crucial for age estimation cannot be obtained. Existing studies on age estimation did not consider the degradation of resolution but used only high-resolution facial images. To overcome this limitation, this paper proposes a deep convolutional neural network (CNN)-based age estimation method that reconstructs low-resolution facial images as high-resolution images using a conditional generative adversarial network (GAN), and then uses the images as inputs. An experiment is conducted using two open databases (PAL and MORPH databases). The results demonstrate that the proposed method achieves higher accuracy in high-resolution reconstruction and age estimation than the state-of-the art methods.
Highlights
Human facial images convey important biological information, including various features such as identity, age, gender, and expression
In details of surveillance application, Ullah et al proposed anomalous entities detection and localization in pedestrian flows based on Gaussian kernel-based integration model (GKIM) [81], and directed sparse graphical model (DSGM) which finds a set of reliable tracks for the targets without relaxation or heuristics and maintains the low computational complexity through the graph design for
As pairs of high-resolution and low-resolution facial images had to be generated in this study, the resolution of the augmented data was decreased by converting high-resolution images of 256 × 256 size to low-resolution images of 8 × 8 size through bilinear interpolation. 29,000 pairs of high-resolution and low-resolution facial images were obtained
Summary
Human facial images convey important biological information, including various features such as identity, age, gender, and expression. For this surveillance application, facial age estimation can be used as supplementary information for the accurate target tracking. Computer-based age estimation using facial images is not as accurate as other types of estimation using identity and gender information. The facial feature space of ages is inhomogeneous, due to the large variation in the non-stationary property of aging and facial appearance across different persons of the same age. To solve this problem, Shen et al propose two deep differentiable random forests methods, deep regression forest (DRF) and deep label
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