By embracing Generative Adversarial Networks (GAN), several face-related applications have significantly benefited and achieved unparalleled success. Inspired by the latest advancement in GAN, we ...propose the PlasticGAN which is a holistic framework for generating images of post-surgery faces as well as reconstruction of faces after surgery completion. This preliminary model works as a helping hand in assisting surgeons, biometric researchers, and practitioners in clinical decision-making by identifying patient cohorts that require building up of confidence with the help of vivid visualizations prior to treatment. It helps them better provide the tentative alternatives by simulating aging patterns. We used the face recognition system for evaluating the same individual with and without masks on surgery face, keeping the current trends in mind such as forensic and security application and recent worldwide COVID scenario. The experimental results suggested that plastic surgery-based synthetic cross-age face recognition (PSBSCAFR) is an arduous research challenge, and state-of-art face recognition systems can negatively affect face recognition performance. This can present a new dimension for the research community.
We propose a novel approach that addresses face aging as an unsupervised image-to-image translation problem. The proposed approach achieves age progression (i.e., future looks) and regression (i.e., ...previous looks) of face images that belong to a specific age class by translating them to other (subsequent or precedent) age classes. It learns pairwise translations between all age classes. Two variants are presented. The first one learns a global transformation, while the second one incorporates a pyramid encoding and decoding scheme to more effectively diffuse age class information. The proposed variants are thoroughly evaluated with respect to both qualitative and quantitative criteria. They yield appealing face age progression and regression results when compared to ground truth images and outperform state-of-the-art approaches for face aging based on quantitative evaluation metrics. Notably, the incorporation of pyramid encoding and decoding is proven to be beneficial to the quality of the generated images.
Most existing state-of-the-art face aging models primarily focus on an adult or long-span aging and modeling age transformation in the image domain. This work proposes a child and adult face aging ...framework that captures more texture and shape information using attention with a wavelet-transformation-based generative adversarial network in the frequency domain. To facilitate child and adult age synthesis, we adopt a wavelet-based multi-scale patch discriminator, which increases the stability of model training and captures local texture details of the child and adult faces. Moreover, we introduce a modified convolutional block attention module, emphasizing only facial regions related to a target attribute and preserving the attribute-excluding details. Our new objective function, modified attention generator, and wavelet multi-scale patch discrimination has shown qualitative and quantitative improvements over the state-of-the-art approaches in terms of face recognition and age estimation on benchmarked children and adult datasets.
The aim of this reprint is to report on new scenarios, technologies, and applications related to the concept of the Internet of Everything and discuss challenges and risks. We introduced a wide range ...of topics related to emerging technologies. This reprint includes research on vendor-managed inventory mechanisms based on the SCADA of the Internet of Things, in-memory computing architecture for a convolutional neural network, face prediction system for missing children in a smart city safety network, anomaly electricity usage behavior in residence using an autoencoder, cloud-edge-smart IoT architecture for speeding up the deployment of neural network models, generative adversarial network and diverse feature extraction methods to enhance the classification accuracy of the tool-wear status, classifying conditions of speckles and wrinkles on the human face using a deep learning approach, repetition with learning approaches in massive machine-type communications, GDPR personal privacy security mechanism for smart home systems, and the development of an autonomous vehicle training and verification system. These articles demonstrate the rapid advances being made in the field of electronics and highlight the potential for these technologies to impact our daily lives.
Excessive exposure to solar ultraviolet (UV) radiation can cause skin cancer. Implementing new technologies and computational algorithms can potentially change the outlook for cancer prevention and ...facilitate early detection of melanoma, thereby reducing mortality. Mobile technology as a potential provider of health services in delivering health information and conducting interventions, especially in skin fields, where a significant part of diagnosis is based on visual examination, can be important. Evidence showed that constructs of protection motivation theory (PMT) were good predictors of practicing sun protection behaviors in students. This study will investigate whether mobile applications improve safe and healthy behaviors and affect students' reduced UV exposure.
This randomized controlled trial will be conducted on 320 students on 06/04/2022 in Zahedan. We created mobile applications (Sunshine and Skin Health and WhatsApp apps). Sunshine and Skin Health app allows users to see their changed faces in three stages of adolescence, middle age, and old age based on sun protection behavior. The WhatsApp app has 27 health messages based on PMT theory, eight educational files, and a skin cancer clip that will be sent through WhatsApp during a week. Randomization will be performed using a 1:1 (control: intervention) ratio. The primary endpoint is the group difference in sun-protective behaviors and PMT constructs immediately after the intervention. The secondary endpoint is the group difference in sun-protective behaviors and PMT constructs at a 3-month follow-up. The data will be analyzed in SPSS.22, and the significance level will be considered at 0.05.
The present study examines the effectiveness of mobile applications in improving sun-protective behaviors. If this intervention enhances sun protection behaviors, it can prevent students' skin damage.
Iranian Registry of Clinical Trials IRCT20200924048825N1. Prospectively registered on 8 February 2021.
Generative Adversarial Networks (GAN) are being increasingly used to perform face aging due to their capabilities of automatically generating highly-realistic synthetic images by using an adversarial ...model often based on Convolutional Neural Networks (CNN). However, GANs currently represent black box models since it is not known how the CNNs store and process the information learned from data. In this paper, we propose the first method that deals with explaining GANs, by introducing a novel qualitative and quantitative analysis of the inner structure of the model. Similarly to analyzing the common genes in two DNA sequences, we analyze the common filters in two CNNs. We show that the GANs for face aging partially share their parameters with GANs trained for heterogeneous applications and that the aging transformation can be learned using general purpose image databases and a fine-tuning step. Results on public databases confirm the validity of our approach, also enabling future studies on similar models.
Face aging is of great importance for cross-age recognition and entertainment-related applications. Recently, conditional generative adversarial networks (cGANs) have achieved impressive results for ...face aging. Existing cGANs-based methods usually require a pixel-wise loss to keep the identity and background consistent. However, minimizing the pixel-wise loss between the input and synthesized images likely resulting in a ghosted or blurry face. To address this deficiency, this paper introduces an Attention Conditional GANs (AcGANs) approach for face aging, which utilizes attention mechanism to only alert the regions relevant to face aging. In doing so, the synthesized face can well preserve the background information and personal identity without using the pixel-wise loss, and the ghost artifacts and blurriness can be significantly reduced. Based on the benchmarked dataset Morph, both qualitative and quantitative experiment results demonstrate superior performance over existing algorithms in terms of image quality, personal identity, and age accuracy. Codes are available on https://github.com/JensonZhu14/AcGAN.
In this paper, a simulation of the human face evolution from youth to old age is presented. In this work, the goal is to maintain a person's appearance and accurately estimate the face at the target ...age; in such a way that the simulated face should be realistic and have the appearance and texture characteristics of the target age. To achieve this goal, the number of feature points and their arrangement on the face are very important. First, we suggest proper feature points. Then, using the active appearance model (AAM) method and the presented model, templates are obtained as representative of different age groups. To maintain the unique geometric characteristics of the input face and apply these characteristics to the target age template, the proposed feature point pattern and the MLS method have been used. Then, using the active appearance model method and having the target age template contain the geometric characteristics of the input face, and the steps of changing the input face age to reach the target age are presented. Finally, the results of the survey, using two methods of age recognition and real image recognition (separately for man and woman images), show an average of 80.77% (male images) and 81.36% (female images) correct answers of the participants in this Poll.
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.
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.
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