Learning Non-Linear Reconstruction Models for Image Set Classification

Munawar Hayat, Mohammed Bennamoun, Senjian An; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1907-1914

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.

Related Material


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[bibtex]
@InProceedings{Hayat_2014_CVPR,
author = {Hayat, Munawar and Bennamoun, Mohammed and An, Senjian},
title = {Learning Non-Linear Reconstruction Models for Image Set Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}