The client proposed advanced software that could take photos of missing people and help family members identify them. The software could even take photos clicked from an angle as input, take the age factor into consideration, and use data to improve the algorithm. The deep learning solution succeeded in finding many missing children and identifying the culprits responsible for these crimes.

Project Summary

Our client wanted to design software to fight human trafficking by helping people locate missing family members and friends. The model was supposed to match the photo of the person to help in identification. The main challenge was the quality of the original photo, the angle at which the photo was taken, and the time elapsed between the photo in the system and the latest photo. We used deep learning to predict the face poses and match the photos. Also, as we added new data, the system learned itself and started providing more accurate results. Many missing people were identified, and suspicious people were verified by the system.

  • Develop and design a project to help in the domain of human trafficking in the states and cities.
  • Aim to help people locate their missing kids or other family members who are tricked into begging or other such activities.
  • Focusing on this, the client wanted a system that could help in matching the photos of a person to get their identity.
  • The photo quality of the person missing might not always be the same as their original photos.
  • The age and duration of the latest photo in the system and the latest captured photo could vary drastically and impact the quality of the results.
  • The angle from which the photo is captured might not always help in finding the best results.
  • This project required identifying the faces in an image as the first step.
  • Then, the prediction of face poses/landmarks was considered.
  • ace encodings and calculations to be done using deep learning techniques to match the images.
  • Capturing a photo and comparing it to the system helped in finding the identity of the people.
  • The system was able to learn itself as new data was added.
  • Suspicious people were easily verified against the data in the system

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