Dynamic Probabilistic Linear Discriminant Analysis For Video Classification

Fabris, Alessandro; Nicolaou, Mihalis; Kotsia, Irene and Zafeiriou, Stefanos. 2017. 'Dynamic Probabilistic Linear Discriminant Analysis For Video Classification'. In: Proceedings of IEEE Int'l Conf. Acoustics, Speech and Signal Processing (ICASSP). New Orleans, United States 5-9 Mar 2017. [Conference or Workshop Item]

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Abstract or Description

Component Analysis (CA) comprises of statistical techniques that decompose signals into appropriate latent components, relevant to a task-at-hand (e.g., clustering, segmentation, classification). Recently, an explosion of research in CA has been witnessed, with several novel probabilistic models proposed (e.g., Probabilistic Principal CA, Probabilistic Linear Discriminant Analysis (PLDA), Probabilistic Canonical Correlation Analysis). PLDA is a popular generative probabilistic CA method, that incorporates knowledge regarding class-labels and furthermore introduces class-specific and sample-specific latent spaces. While PLDA has been shown to outperform several state-of-the-art methods, it is nevertheless a static model; any feature-level temporal dependencies that arise in the data are ignored. As has been repeatedly shown, appropriate modelling of temporal dynamics is crucial for the analysis of temporal data (e.g., videos). In this light, we propose the first, to the best of our knowledge, probabilistic LDA formulation that models dynamics, the so-called Dynamic-PLDA (DPLDA). DPLDA is a generative model suitable for video classification and is able to jointly model the label information (e.g., face identity, consistent over videos of the same subject), as well as dynamic variations of each individual video. Experiments on video classification tasks such as face and facial expression recognition show the efficacy of the proposed method.

Item Type:

Conference or Workshop Item (Paper)

Additional Information:

This work was partially funded by the FiDiPro program of Tekes (project number: 1849/31/2015), as well as by the European Community Horizon 2020 [H2020/2014-2020] under grant agreement no. 688520 (TeSLA).


Probabilistic Linear Discriminant Analysis, Face Recognition, Component Analysis

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1 December 2016Accepted
1 March 2017Published

Event Location:

New Orleans, United States

Date range:

5-9 Mar 2017

Item ID:


Date Deposited:

11 Apr 2017 14:40

Last Modified:

29 Apr 2020 16:26



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