@inproceedings{4d8ab954bee74628a92b15818c2e6343,
title = "Fisher vector encoded deep convolutional features for unconstrained face verification",
abstract = "We present a method to combine the Fisher vector representation and the Deep Convolutional Neural Network (DCNN) features to generate a rerpesentation, called the Fisher vector encoded DCNN (FV-DCNN) features, for unconstrained face verification. One of the key features of our method is that spatial and appearance information are simultaneously processed when learning the Gaussian mixture model to encode the DCNN features. Evaluations on two challenging verification datasets show that the proposed FV-DCNN method is able to capture the salient local features and also performs well when compared to many state-of-the-art face verification methods.",
author = "Chen, {Jun Cheng} and Jingxiao Zheng and Patel, {Vishal M.} and Rama Chellappa",
year = "2016",
month = aug,
day = "3",
doi = "10.1109/ICIP.2016.7532906",
language = "English (US)",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2981--2985",
booktitle = "2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings",
address = "United States",
note = "23rd IEEE International Conference on Image Processing, ICIP 2016 ; Conference date: 25-09-2016 Through 28-09-2016",
}