@inproceedings{b62a4eeead9e4098ade128fcd274a353,
title = "Landmark-based fisher vector representation for video-based face verification",
abstract = "Unconstrained video-based face verification is a challenging problem because of dramatic variations in pose, illumination, and image quality of each face in a video. In this paper, we propose a landmark-based Fisher vector representation for video-to-video face verification. The proposed representation encodes dense multi-scale SIFT features extracted from patches centered at detected facial landmarks, and face similarity is computed with the distance measure learned from joint Bayesian metric learning. Experimental results demonstrate that our approach achieves significantly better performance than other competitive video-based face verification algorithms on two challenging unconstrained video face dataseis, Multiple Biometric Grand Challenge (MBGC) and Face and Ocular Challenge Series (FOCS).",
keywords = "Fisher vector, face verification, facial landmarks",
author = "Chen, {Jun Cheng} and Rama Chellappa",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Image Processing, ICIP 2015 ; Conference date: 27-09-2015 Through 30-09-2015",
year = "2015",
month = dec,
day = "9",
doi = "https://doi.org/10.1109/ICIP.2015.7351294",
language = "English (US)",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2705--2709",
booktitle = "2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings",
address = "United States",
}