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#Face morph age progression applications skin
The proposed technique can efficiently diagnose the skin illness using restricted features mined as ROI, assess the stage of wrinkles and analyze the stage of wrinkles. A classifier is intended to offer improved accuracy for identification when it is targeted at a specific problem. Previously, wrinkles in the ROI were identified by using a pattern recognition algorithm. Based on noticed wrinkles on the skin, the facial structures are removed to find ROI. The proposed method initially detects the wrinkles by using facial images.
#Face morph age progression applications software
The software design of the front end and the backend details is displayed along with the result screenshots. Also, this research work will provide a detailed description about the selected test images and database. AI, deep learning and CNN techniques are incorporated to achieve fast performance system. The wrinkles on the skin, which gets increased based on the age, are being used as the discriminating factor to predict the age of the human being by using the images. The proposed method is more focused on the wrinkles detection based on convolution neural network. The proposed work describes a novel method for predicting age and wrinkles by using image processing and other advanced technologies. Since different applications are available for estimating the age based on the facial images and other skin-related factors, the main aim of this research study is to use deep CNN for detecting the wrinkles in the human skin. Also, these methods require a lot of human interference. Maximum of the conventional wrinkles examination schemes is semi-automatic. In any medical cosmetology, skin analysis becomes an important procedure for the wrinkle detection or any other medical problems. The primary focus of this research work is to study the appearance of face wrinkles, which is considered as one of the most noticeable changes that happen as people become older. The paper focuses on the various practical applications of super-resolution face aging using Generative Adversarial Networks (GANs).Īging is a natural process that affects the human body. The proposed work is evaluated with quantitative and qualitative methods, produces synthesized face aged images with a 0.001% error rate, and is also evaluated with the comparison to prior methods. Moreover, the experimental results are obtained from five publicly available datasets: UTKFace, CACD, FGNET, IMDB-WIKI, and CelebA. Thus, presents more detailed information in an image because of its high quality. Then image sharpening with edge enhancement is done to give high-quality input to SRGAN, which further generates the super-resolution face aged images. Further, the regex filtering process is performed to separates the synthesized face images from the output of AttentionGAN. Then attention mask is multiplied with the corresponding content mask along with an input image to finally achieve the desired results. One subnet for generating multiple attention masks and the other for generating multiple content masks. AttentionGAN uses two separate subnets in a generator. So, to solve the issue, the proposed work focuses on the realistic face aging method using AttentionGAN and SRGAN. Research has been continuing in face aging to handle the challenge to generate aged faces accurately. The existing face age progression approaches have the key problem of unnatural modifications of facial attributes due to insufficient prior knowledge of input images and nearly visual artifacts in the generated output. In today’s world that demands more security and a touchless unique identification system, face aging attains tremendous attention. The results showed that automating the process can effectively detect the malaria parasite in blood samples with an accuracy of over 94% with less complexity than the previous approaches found in the literature.įace age progression, goals to alter the individual’s face from a given face image to predict the future appearance of that image. Extensive performance analysis is presented in terms of precision, recall, f-1score, accuracy, and computational time.
![face morph age progression applications face morph age progression applications](https://www.cs.virginia.edu/~nn4pj/AP_sample.jpg)
Then Decision Tree, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour classifiers were trained using these six features. Six different features-VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 models-were extracted. For this reason, this study proposes the use of machine-learning models to detect the malaria parasite in blood-smear images. Due to the high number of cases and lack of sufficient diagnostic facilities and experienced medical personnel, there is a need for advanced diagnostic procedures to complement existing methods. Malaria is one of the most infectious diseases in the world, particularly in developing continents such as Africa and Asia.