@inproceedings{1569cffb8628420ea3e55e680b2f8aa8,
title = "Learning non-linear reconstruction models for image set classification",
abstract = "We propose a deep learning framework for image set classification with application to face recognition. An Adaptive Deep Network Template (ADNT) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The pre-initialized ADNT is then separately trained for images of each class and class-specific models are learnt. Based on the minimum reconstruction error from the learnt class-specific models, a majority voting strategy is used for classification. The proposed framework is extensively evaluated for the task of image set classification based face recognition on Honda/UCSD, CMU Mobo, YouTube Celebrities and a Kinect dataset. Our experimental results and comparisons with existing state-of-the-art methods show that the proposed method consistently achieves the best performance on all these datasets.",
keywords = "face recognition, pattern recognition, computer vision, Image Set Classification, Deep Learning, Face Recognition",
author = "Munawar Hayat and Mohammed Bennamoun and Senjian An",
year = "2014",
month = jun,
day = "23",
doi = "10.1109/cvpr.2014.246",
language = "English",
isbn = "9781479951178",
volume = "1",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1915--1922",
editor = "Munawar Hayat",
booktitle = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
address = "United States",
note = "2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 ; Conference date: 23-06-2014 Through 28-06-2014",
}