Review — Learning a Similarity Metric Discriminatively, with Application to Face Verification

Contrastive Loss + LeNet-Like CNN Siamese Network for Face Recognition

Siamese Network for Face Recognition (Figure from https://www.latentview.com/blog/siamese-neural-network-a-face-recognition-case-study/)

Outline

1. Siamese Network Architecture

Network Architecture Gw(X)

2. Contrastive Loss Function

Contrastive Loss (Figure from [2020 J Pathol Inform] Constellation Loss: Improving the efficiency of deep metric learning loss functions for optimal embedding)

3. Experimental Results

3.1. Datasets

Top: Images from AT&T dataset. Middle: Images from the AR dataset. Bottom: Images from FERET dataset. Each graphic shows a genuine pair, an impostor pair and images from a typical subject.
Details of the validation and test sets for the two datasets

3.2. Results

False reject percentage for different false accept percentages
AT&T dataset: percent false reject vs. false accept
AR/Purdue dataset: percent false reject vs. false accept

PhD, Researcher. I share what I've learnt and done. :) My LinkedIn: https://www.linkedin.com/in/sh-tsang/, My Paper Reading List: https://bit.ly/33TDhxG