Humans of Paris 1900

Motivation

We take inspiration from the famous Instagram page, Humans of New York, which features pictures and stories of people living in current day New York. In similar fashion, our project, Humans of Paris, has the aim to be a platform to connect us to the people of 19th century Paris. Photography was still in its early stages when Nadar took up the craft in his atelier in Paris. Through the thousands of pictures taken by him and his son we can get a glimpse of who lived at the time. We explore the use of deep learning models to cluster similar faces to get an alternative, innovative view of the collection and allowing for serendipitous discovery of patterns and people. There is a story behind every person, and our interface highlights this by association people’s story with their picture.

Project plan

The final goal is to present the user with a way to navigate the collection and understand who the people is the photos are and what life they lived. We develop a website that provides the user with 4 core functionalities.

1. An overview of the most famous individuals in the collection
2. A search page to explore individuals based on tag matching
3. A cluster view of individuals in the database, based on their looks
4. A way to upload ones image and find ones’ doppelgänger in the collection

To make this vision reality, we went through 4 stages in the processing, namely fetching the data, processing it, storing and serving data and front end development.

The fetching can easily be done using the wrapper provided by Raphaël Barman.

• Preprocessing involves processing of the metadata to extract tags as well as processing of the images to obtain a vector encoding of an individual's face using OpenFace, extracting age and gender using the pyAgender python library and cropping it using OpenCV.
• Storing the data involves Sqlite and Django ORM Django ORM
• Serving the data: Django
• Front end development uses Bootstrap to enable quick and beautiful UI design and D3.js for displaying the FaceMap.
We organized the tasks according to the following schedule and milestones.

Milestones

Weekly working plan

Timeframe	Task	Completion
Week 4
07.11	Understanding Gallica Query Gallica API	✓
	Query Gallica API
Week 5
14.10	Start preprocessing images	✓
	Choose suitable Wikipedia API
Week 6
21.10	Choose face recognition library	✓
	Get facial vectors
	Try database design with Docker & Flask
Week 7
28.10	Remove irrelevant backgrounds of images	✓
	Extract age and gender from images
	Design data model
	Extract tags, names, birth and death years out of metadata
Week 8
04.11	Set up database environment	✓
	Set up mockup user-interface
	Prepare midterm presentation
Week 9
11.11	Get tags, names, birth and death years in ready-to-use format	✓
	Handle Wikipedia false positives
	Integrate face recognition functionalities into database
Week 10
18.11	Create draft of the website (frontend)
	Create FaceMap using D3
	Week 11
25.11	Integrate all functionalities
	Finalize project website
	Week 12
02.12	Write Project report