Computer Vision2020 – 2021ML Engineer
Face Recognition (Siamese / Triplet Loss)
High accuracy from a small set of user photos
Facial-recognition model using a Siamese network and triplet loss so a small number of user photos is enough to reach high accuracy. Built on Inception blocks in TensorFlow / Keras with OpenCV for capture.
The Problem & Engineering Constraint
The Core Challenge
Classic classifiers want large labelled galleries. A personal recognition system has to work from a handful of enrolment images.
Technical Architecture & Approach
Engineering Solution & Implementation
Siamese architecture with triplet loss on Inception_blocks_v2, TensorFlow/Keras training, and OpenCV (cv2) for image intake.
Measured Production Impact
Verified Outcomes & Deliverables
Enrolment from minimal user photos rather than a large labelled set.
Triplet-loss embedding suited to one-shot / few-shot recognition.
Technologies & Components
System Tooling & Technologies
PythonTensorFlowKerasOpenCVInception v2NumPy