Person Recognition using Facial Micro-Expressions with Deep Learning
Tuval Kay Yuval Ringel Khen Cohen Mor-Avi Azulay David Mendlovic
This study investigates the efficacy of facial micro-expressions as a soft
biometric for enhancing person recognition, aiming to broaden the understanding
of the subject and its potential applications. We propose a deep learning
approach designed to capture spatial semantics and motion at a fine temporal
resolution. Experiments on three widely-used micro-expression databases
demonstrate a notable increase in identification accuracy compared to existing
benchmarks, highlighting the potential of integrating facial micro-expressions
for improved person recognition across various fields.