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Team Name:

Artificially Intelligent


Team Members:


Evidence of Work

Interactive Culture

Project Info

Team Name


Artificially Intelligent


Team Members


5 members with unpublished profiles.

Project Description


This project addresses two SA based challenges, Digital Culture and Physical Culture. Consequently our project is made of two parts. The first is a website that does neural style transfer to convert curent day image back in time to colonist era with the image style of your choice. The second is inteactive exhibit that will match your face with the face of a colonist.


Data Story


Interactive Culture

This project addresses two SA based challenges, Digital Culture and Physical Culture. Consequently our project is made of two parts. The first is a website that does neural style transfer to convert current day image back in time to colonist era with the image style of your choice. The second is interactive exhibit that will match your face with the face of a colonist.

Digital Culture - Neural Style Transfer

This project used both the old colonists photographs dataset from the State library of South Australia and the South Australian Government Photographic Collection from the History Trust of South Australia. To preprocess the data we used a facial detection and recognition system to automatically detect people in photographs and crop them out to an appropriate size.

https://data.sa.gov.au/data/dataset/south-australian-government-photographic-collection

https://data.sa.gov.au/data/dataset/old-colonists-photographs

Physical Culture - Colonist Face Matching

This project is a physical interactive exhibit that uses a camera and machine learning to show you which Adelaide colonist you most look like. Simply stand in front of the camera and a similar looking colonist will immediately be shown on the screen.

The past doesn't feel that far away when you know there was someone that looked like you walking around.

--Colonists Portrait Images

https://data.sa.gov.au/data/dataset/old-colonists-photographs

We found this dataset on the state library that had just over 1000 old colonist portraits. The dataset consisted of csv files that contained info about each photo and a download URL. There were separate csv for men and women.

We used python and pandas to load the csv files and join them together in one big dataframe. We iterated over each row in the pandas dataframe and downloaded each colonist image from the web. The images were saved with the record id as the name so that data association could be made later if needed.

--FaceNet Features

https://pypi.org/project/facenet-pytorch/
https://github.com/timesler/facenet-pytorch

FaceNet is a open source project that uses a deep convolution neural network to convert an image of a face into a feature vector. The feature vectors are 512 elements long and encode all the unique features about a face. The training algorithm that was used ensures that every person gets a unique feature vector for their face. To check if two faces are similar you can simply compute the L2 distance between two vectors.

--Colonist Portrait Face Features

To quickly match faces we used a python script and FaceNet to precompute a feature vector for every colonist image. The feature vectors were saved in a python dictionary that is indexed by the record id.

--Real Time Predictions

We use python and OpenCV to capture images live from the webcam and use FaceNet to compute a feature vector for every frame that has a face in it. We then compute the L2 distance between this feature vector and every feature vector from the colonist portraits. The colonist that has the smallest L2 distance has the most similar face and shown to screen.


Evidence of Work

Video

Team DataSets

Old Colonists photographs

Data Set

South Australian Government Photographic Collection

Data Set

Challenge Entries

Digital Culture

How do we make our digital cultural heritage collections engaging for online audiences? What experiences should we be developing beyond the search and retrieve box to visualise gallery, library and museum collections online and encourage their reuse and good storytelling?

Eligibility: Must use at least 2 datasets from 2 separate institutions that make up the North Terrace Cultural Precinct Innovation Lab

Go to Challenge | 14 teams have entered this challenge.

Physical Culture

How might we better integrate our digital collections and datasets into our physical gallery, library, or museum spaces?

Eligibility: Must use at least 2 datasets from 2 separate institutions that make up the North Terrace Cultural Precinct Innovation Lab

Go to Challenge | 9 teams have entered this challenge.

Showcasing our regions

How might we promote South Australian regions to boost regional development?

Eligibility: Use any open dataset to support your entry.

Go to Challenge | 16 teams have entered this challenge.