Arani Aravinthan and Madushi Pathmaperuma, University of Central Lancashire, UCL
Beauty is a power which allows people to express themselves, gain self-confidence and open to others. Usage of beauty products can help this by creating a new look to uplift the character. Choosing the right makeup product is not an easy task in this diverse range of products these days. Intelligent systems for beauty and makeup selection have gained significant research interest in recent years. Most existing models focus on detecting prominent facial features such as skin tone, lip colour, and overall facial structure. However, minor yet impactful areas, such as the delicate regions around the eyes, are often overlooked. These areas play a crucial role in defining facial aesthetics, influencing expressions, and enhancing overall appearance. To address this gap, this system is designed to provide targeted recommendations for eye-focused beauty enhancements, ensuring a more comprehensive and personalized approach to makeup selection. The proposed system will recommend makeup products considering personal traits of the user such as the length and volume of the eyelashes. A new approach has been devised in calculating the length of the eyelashes aiding the use of advanced computer vision techniques like edge detection, and a regression-based Convolutional Neural Network (CNN) model is trained for prediction. A Support Vector Machine (SVM) is used for the classification task in recommending products for eyelash care.
Edge Detection, Contour Detection, Support Vector Machine.
Massimiliano Barone, STMicroelectronics, Agrate Brianza, Milano, Italy
The goal of this paper is to present a new, fast, and sub-optimal technique for splitting overly large regions of interest (ROIs) or the original full image. ROIs are typically box areas containing significant image information. The aim is to generate ROIs based on a specific criterion for subsequent focused analysis. The proposed technique converts a generic ROI size or the original full image into a few smaller, fixed-size ROIs, fully covering all significant details, with a maximum interception over union and a high number of details for each new ROI. Where details typically come from a previous feature extraction or image segmentation. This method is particularly helpful for convolutional neural networks (CNN) with fixed-size input images and datasets requiring image portions with as much pertinent information as possible. The effectiveness is compared against dividing the original ROI into a regular grid of ROIs, using the number of ROIs and the average detail density per ROI.
Regions of interest, ROI, clustering, segments, computer vision, regional proposal.
Sun Xiaoping and Rodolfo C. Raga Jr, National University, Philippines
This paper proposes an air quality prediction model ST-HyDM based on spatiotemporal data mining and hybrid deep learning. By constructing a dynamic graph neural network to capture spatiotemporal dependencies, fusing multimodal features, and using a dual-branch architecture of Transformer and TCN for feature extraction and prediction. At the same time, a missing value processing mechanism based on generative adversarial networks (GAN) is introduced to improve data integrity and model performance. Experimental results show that compared with traditional models such as linear regression and random forest, ST-HyDM has higher accuracy and robustness in air quality prediction, providing a more effective solution for air quality prediction.
Air quality prediction, spatiotemporal data mining, hybrid deep learning, dynamic graph neural network, missing value processing.
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