Edge Computing and Al Intern opportunity

Abstract

Robust and efficient face recognition and verification is becoming increasingly important in the modern age, due to the escalating presence of online interactions. For example, a manager may want to check remote employee authentication, but only has a video feed of them, or outside intrusions in secure environments. Thus, Face recognition and verification programs must be optimized. To achieve this, this internship utilized the deepface framework for python. The contemporary facial recognition pipeline consists of four core stages: detection, alignment, representation, and verification. Experiments involving distinct combinations of facial recognition models, face detectors, distance metrics and alignment models performed on the Labelled Faces in the Wild database were conducted in order to optimize the facial recognition pipeline. This internship provided valuable practical experience in this field and equipped me with necessary skills to contribute meaningfully