There is strong evidence for the effectiveness of addressing tobacco use in health care settings. However, few smokers receive cessation advice when visiting a hospital. Implementing smoking ...cessation technology in outpatient waiting rooms could be an effective strategy for change, with the potential to expose almost all patients visiting a health care provider without preluding physician action needed.
The objective of this study was to develop an intervention for smoking cessation that would make use of the time patients spend in a waiting room by passively exposing them to a face-aging, public morphing, tablet-based app, to pilot the intervention in a waiting room of an HIV outpatient clinic, and to measure the perceptions of this intervention among smoking and nonsmoking HIV patients.
We developed a kiosk version of our 3-dimensional face-aging app Smokerface, which shows the user how their face would look with or without cigarette smoking 1 to 15 years in the future. We placed a tablet with the app running on a table in the middle of the waiting room of our HIV outpatient clinic, connected to a large monitor attached to the opposite wall. A researcher noted all the patients who were using the waiting room. If a patient did not initiate app use within 30 seconds of waiting time, the researcher encouraged him or her to do so. Those using the app were asked to complete a questionnaire.
During a 19-day period, 464 patients visited the waiting room, of whom 187 (40.3%) tried the app and 179 (38.6%) completed the questionnaire. Of those who completed the questionnaire, 139 of 176 (79.0%) were men and 84 of 179 (46.9%) were smokers. Of the smokers, 55 of 81 (68%) said the intervention motivated them to quit (men: 45, 68%; women: 10, 67%); 41 (51%) said that it motivated them to discuss quitting with their doctor (men: 32, 49%; women: 9, 60%); and 72 (91%) perceived the intervention as fun (men: 57, 90%; women: 15, 94%). Of the nonsmokers, 92 (98%) said that it motivated them never to take up smoking (men: 72, 99%; women: 20, 95%). Among all patients, 102 (22.0%) watched another patient try the app without trying it themselves; thus, a total of 289 (62.3%) of the 464 patients were exposed to the intervention (average waiting time 21 minutes).
A face-aging app implemented in a waiting room provides a novel opportunity to motivate patients visiting a health care provider to quit smoking, to address quitting at their subsequent appointment and thereby encourage physician-delivered smoking cessation, or not to take up smoking.
Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks. In this study, we review a ...classic approach that uses a generative adversarial network. The structure, formulation, learning algorithm, challenges, advantages, and disadvantages of the algorithms contained in each proposed algorithm are discussed systematically. Generative Adversarial Networks are an approach that obtains the status of the art in the field of face aging by adding an aging module, paying special attention to the face part, and using an identity-preserving module to preserve identity. In this paper, we also discuss the database used for facial aging, along with its characteristics. The dataset used in the face aging process must have the following criteria: (1) a sufficiently large age group in the dataset, each age group must have a small range, (2) a balanced distribution of each age group, and (3) has enough number of face images.
Learning-based face aging/rejuvenation has witnessed rapid progress in recent years. However, existing methods still suffer from the loss of personalized identity information when synthesizing ...cross-age faces. In this paper, we propose a Conditional Adversarial Consistent Identity AutoEncoder (CACIAE) to revisit this problem. Firstly, a Res-Encoder is designed to better generate powerful face representation. Secondly, the rectangular kernel is introduced into the encoder to make full use of horizontal continuous characteristic information of faces and to make the synthetic face images more natural. Thirdly, a novel consistent identity loss is proposed to learn more face details and produce more natural identity-preserving images. Further, two discriminators are designed to enforce the generator to generate more realistic and more age-accurate images. Experimental results prove the effectiveness of the proposed method, both qualitatively and quantitatively. The code is available at https://github.com/XH-B/CACIAE.
Face aging is one of the most interesting style transfer ideas due to the extraordinary development in image synthesis succeeded by deep learning models that is the generative adversarial networks ...and its marvelous impact on practical applications such as finding missing child after few years, smart voting where we have to update the data based on the age changes of people. The existing face aging methods have proven the achievement in the case of the paired image dataset. Collecting the paired data samples of different age groups is hard and expensive. Encouraged by GAN's success in a variety of fields for image-to-image conversion problems. The main aim of this paper is to keep the original identity as it is in the face aging problem. We have designed an approach known as the multi-scale feature fusion model followed by a residual network to generate images of a person based on different age conditions. We worked on an unpaired image dataset because we do not have the dataset of the same person in different age categories. In this paper, we have used the UTKFace dataset which is publicly available. The scope of research is to consider only two age categories. The results are obtained by performing experiments and through a survey of people which indicates the modern method for face age progression and regression.
The objective of face aging is to generate facial images that present the effects of aging. The existing one-hot encoding method for aging and/or rejuvenation patterns overlooks the personalized ...patterns for different genders and races, causing errors such as a male beard appearing on an aged female face. A gender-preserving face aging model is proposed to address these issues, termed GFAM. GFAM employs a generative adversarial network and includes several subnetworks that simulate the aging process between two adjacent age groups to learn specific aging effects. Specifically, the proposed model introduces a gender classifier and gender loss function that uses gender information as a self-guiding mechanism for maintaining gender attributes. To maintain the identity information of synthetic faces, the proposed model also introduces an identity-preserving module. Additionally, age balance loss is used to mitigate the impact of imbalanced age distribution and enhance the accuracy of aging predictions. Moreover, we construct a dataset with balanced age distribution for the task of face age progression, referred to as Age_FR. This dataset is expected to facilitate current research efforts. Ablation studies have been conducted to extensively evaluate the performance improvements achieved by our method. We obtained relative improvements of 3.75% higher than the model without the gender preserving module. The experimental results provide evidence of the effectiveness of the proposed method, both through qualitative and quantitative analyses. Notably, the mean face verification accuracy for the age-progressed groups (0–20, 31–40, 41–50, and 51–60) was found to be 100%, 99.83%, 99.79%, and 99.11%, respectively, highlighting the robustness of our approach across various age ranges.
Face aging is of great importance for the information forensics and security fields, as well as entertainment-related applications. Although significant progress has been made in this field, the ...authenticity, age specificity, and identity preservation of generated face images still need further discussion. To better address these issues, a Feature-Guide Conditional Generative Adversarial Network (FG-CGAN) is proposed in this paper, which contains extra feature guide module and age classifier module. To preserve the identity of the input facial image during the generating procedure, in the feature guide module, perceptual loss is introduced to minimize the identity difference between the input and output face image of the generator, and L2 loss is introduced to constrain the size of the generated feature map. To make the generated image fall into the target age group, in the age classifier module, an age-estimated loss is constructed, during which L-Softmax loss is combined to make the sample boundaries of different categories more obvious. Abundant experiments are conducted on the widely used face aging dataset CACD and Morph. The results show that target aging face images generated by FG-CGAN have promising validation confidence for identity preservation. Specifically, the validation confidence levels for age groups 20–30, 30–40, and 40–50 are 95.79%, 95.42%, and 90.77% respectively, which verify the effectiveness of our proposed method.
This paper deals with Generative Adversarial Networks (GANs) applied to face aging. An explainable face aging framework is proposed that builds on a well-known face aging approach, namely the ...Conditional Adversarial Autoencoder (CAAE). The proposed framework, namely, xAI-CAAE, couples CAAE with explainable Artificial Intelligence (xAI) methods, such as Saliency maps or Shapley additive explanations, to provide corrective feedback from the discriminator to the generator. xAI-guided training aims to supplement this feedback with explanations that provide a "reason" for the discriminator's decision. Moreover, Local Interpretable Model-agnostic Explanations (LIME) are leveraged to provide explanations for the face areas that most influence the decision of a pre-trained age classifier. To the best of our knowledge, xAI methods are utilized in the context of face aging for the first time. A thorough qualitative and quantitative evaluation demonstrates that the incorporation of the xAI systems contributed significantly to the generation of more realistic age-progressed and regressed images.
The progression/regression of facial age can be applied to cross-age recognition or entertainment-related applications. It is challenging due to lack of facial expressions of the same person in a ...longer age range. Conditional Adversarial Autoencoder (CAAE) can learn facial manifolds and achieve smooth age development and regression at the same time. Since the generated face is different from the real face, we develop a novel model based on CAAE, which used two discriminators instead of one to solve this problem. The proposed model can produce better results and improve the similarity degree of age progression for different races of people.
The use of computer simulation to understand how human faces age has been a growing area of research since decades. It has been applied to the search for missing children as well as to the fields of ...entertainment, cosmetics and dermatology research. Our objective is to elaborate a model for the age-related changes of visual cues which affect the perception of age, so that we may better predict them. Traditional approaches based on the Active Appearance Model (AAM) tend to blurry appearance and wipe out texture details such as wrinkles. We introduce Wrinkle Oriented Active Appearance Model (WOAAM) where a new channel is added to the AAM dedicated to analyze wrinkles. Firstly, we propose to represent both the shape and texture of each wrinkle on a face by a compact and interpretable vector. Afterwards, to model the distribution of wrinkles on a face, we introduce a new way to approximate an empiric joint probability density by creating an ensemble of joint probability densities estimated by Kernel Density Estimation. Finally, we show how to create new samples from such an ensemble of densities, and thus synthesize new plausible wrinkles. In comparison to other methods which add wrinkles at post-processing level, our method fully integrates them in AAM. Thereby, the wrinkles generated are statistically representative of a specific age in terms of number, length, shape and intensity. With an age estimation Convolutional Neural Network, we found that age-progressed faces produced by the WOAAM better reduces the gap between the expected age and the estimated age than those produced by a classic AAM.
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