Unique User Identification Across Multiple Social Networks

A tool that finds the same person on different social networks by comparing profile details, bringing a user's friends from several sites into one place.

A camera or uploaded image provides the input frame. OpenCV pre-processes it: grayscale conversion, noise removal and resizing. A detector or recognition model finds and identifies the region of interest. The result is displayed, logged in the database and can trigger an alert or action.

Capture the frame, convert to grayscale, remove noise with a Gaussian blur, segment the region of interest with thresholding or edge detection, extract features and finally classify or match them.

First a detector (Haar cascade, HOG or a CNN) finds the face. Then a model converts it into an embedding, a list of numbers that describes the face, and compares it with stored embeddings using a distance measure.

Packet delivery ratio, end-to-end delay, throughput, packet loss and energy consumption, compared between the existing protocol and the proposed one.

Proactive protocols (DSDV, OLSR) keep routes to all nodes updated at all times. Reactive protocols (AODV, DSR) find a route only when data needs to be sent, which saves overhead in mobile networks.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.