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  1. This paper presents facial detection and emotion analysis software developed for use in the classroom, even if you are a beginner or intermediate coder. The goal is to provide a tool that reduces the time teachers spend taking attendance while also collecting data that improves teaching practices. Disturbing current trends regarding school shootings motivated the inclusion of emotion recognition so that teachers are able to better monitor students’ emotional states over time. This will be accomplished by providing teachers with early warning notifications when a student significantly deviates in a negative way from their characteristic emotional profile. This project was designed to give students and teachers a hands- on project to implement in the classroom for the purpose of learning to use concepts from programming, computer vision, and machine learning. It is this team’s hope that the code presented will serve to save teachers time, help teachers better address student mental health needs, and motivate students and teachers to learn more computer science, computer vision, and machine learning as they use and modify the code in their own classrooms. Important takeaways from initial test results are that increasing training images increases the accuracy of the recognition software, and the farther away a face is from the camera, the higher the chances are that the face will be incorrectly recognized. The software tool is available for download at https://github.com/ferrabacus/Digital-Class. 
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  2. As tattooing becomes more popular, so too does tattoo removal. Removal is painful, expensive, and not always effective. To avoid tattooing that customers will later regret, potential customers could use augmented/mixed reality to preview tattoo designs. This paper explores technical tools and methods using computer vision to overlay a digital image, like a tattoo design, on top of frames from a live video feed of the user’s body. Using an RGB camera and depth camera, in this case the Microsoft Kinect, body position and volumetric data are captured. Two methods are presented for using this data for tattoo preview along with the methods’ pros, cons, and state of development. This project was developed by and for high school students and educators to bridge the gap between secondary students and interesting new technology previously reserved for university-level studies. 
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  3. A recommendation system is a program that utilizes techniques to suggest to a user items that they would likely prefer. This paper focuses on an approach to improving music recommendation systems, although the proposed solution could be applied to many different platforms and domains, including Youtube (videos), Netflix (movies), Amazon (shopping), etc. Current systems lack adequate efficiency once more variables are introduced. Our algorithm, Tunes Recommendation System (T-RECSYS), uses a hybrid of content-based and collaborative filtering as input to a deep learning classification model to produce an accurate recommendation system with real-time prediction. We apply our approach to data obtained from the Spotify Recsys Challenge, attaining precision scores as high as 88% at a balanced discrimination threshold. 
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  4. Contemporary developments in computer vision and artificial intelligence show promise to greatly improve the lives of those with disabilities. In this paper, we propose one such development: a wearable object recognition device in the form of eyewear. Our device is specialized to recognize items from the produce section of a grocery store, but serves as a proof of concept for any similar object recognition wearable. It is user friendly, featuring buttons that are pressed to capture images with the built-in camera. A convolutional neural network (CNN) is used to train the object recognition system. After the object is recognized, a text-to-speech system is utilized to inform the user which object they are holding in addition to the price of the product. With accuracy rates of 99.35%, our product has proven to successfully identify objects with greater correctness than existing models. 
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