Background

In today’s Internet environment, privacy is a really important topic. When participating in lots of social media, instant messaging apps, etc, we are often asked to upload or be motivated to share our looks — like Twitter, Instagram, or Weibo, WeChat. However, the multimedia sharing is a high threat to our privacy. To hide their personal information from strangers, some users block viewing requests from unfamiliars, or put mosaic on parts of their face. However, the mosaic is not always the best, since it also block the expressions on user’s face. So, I want to find a way in which we can keep our privacy as well as our expressions.

Project

So, here comes my proposed project Mojisaic (Emoji Mosaic) — a network that detects the expressions or emotions on the face to select a right emoji. (It is a image classifier basically). Then, using library like OpenCV to replace the faces in the image by emojis. The network implements CNNs, as well as Facial Feature Point detections, and classify the emotions as happy, sad, angry, crying, etc.

Reference

1. https://towardsdatascience.com/face-detection-recognition-and-emotion-detection-in-8-lines-of-code-b2ce32d4d5de
2. https://github.com/priya-dwivedi/face_and_emotion_detection
3. https://arxiv.org/abs/1503.03832
4. https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data

CartoonGAN in Web

Background

    Generative adversarial network(GAN) has been widely used for tasks like image/audio generation, image to image translation, and 3D model reconstruction. GAN has become a commonly used tool for generating things. It is also a great help for doing style transfer for high-resolution images.  Though, the trade-off is that such a model requires a large number of computational resources.  Researchers have done a lot of jobs to mitigate the gap between cartoon and real image. And in this paper, we want to bring CartoonGAN[1] to the browser, optimize it, and make applications on top of it.

Motivation

TensorFlow.js makes performing deep learning projects within the browser possible. And the work of CartoonGAN[1] allows us to transfer any cartoon styles we want. Thus, it will be a great combination of web and CartoonGAN so that users can transfer the styles they want on the web.

References

[1] CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

Background

Something I think about a lot: most people make an enormous effort to avoid death and even things that vaguely remind them of death.  I don’t think this is a new phenomenon, but at the same time it seems that sometimes people think that continuing technological advancements will help to distance themselves from it by prolonging life, maybe in hopes of eluding death altogether. Many people spend exorbitant amounts of money to look younger, or some to live their ideas of healthier lives. There’s no wrong or right in these decisions, but I think it’s really interesting to step back and assess them in order to understand how our thoughts about death impact the way we live.  For me, it’s been even more beneficial to think about death on a regular basis, and to not necessarily view it as good or bad, but to accept its inevitability and to maybe grow comfortable with it.

Project

I’m kind of inspired by an app called WeCroak that sends notifications 5 times a day with quotes, essentially just little reminders about death; for my project, I really want to make something that just reminds people that they (and everything around them) will die at some point through the use of machine learning. What I’d like to do is create a sort of image classification project, but instead of labelling objects with names, they will be labelled with lifespans.

Initially I thought it could be really interesting for it to be able to guess the remaining time left in an object’s lifespan, but for that to work it would have to know object lifespan – object age = object remaining time. I think this could be next to impossible to achieve, especially for inanimate objects. To add to this, even though there are beta versions of age guessing programs for humans, I think these come with a lot of ethical problems before even being implemented into another project, in addition to already being inaccurate. There’s something a little too extreme about a death countdown when all I want is to provide a gentle reminder.

Methodology

With just providing lifespans as labels, the project seems like it will mostly be a matter of manipulating datasets. I don’t really have much experience with that, which is why I think it would be a good opportunity to learn. Because it seems really tedious to try to do this manually, and maybe not possible to finish in the time given, I’m going to do some research to see if there is an API for objects’ lifespans that I can potentially weave in with the image recognition to avoid this.

Reference

WeCroak app: https://www.wecroak.com

Group Members  –  Casey, Eric

Background

Generative Adversarial Network(GAN) is a mature and widely applied machine learning technique, in topics including but not limited to Image generation/translation/style transfer, constantly proving GAN’s power in generative work. Research has been done on realizing style transfer from real image to cartoon. CartoonGAN[1] is a powerful network that performances such style transfer on high resolution images, powered by TensorFlow 2.0 Alpha.

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