Emotion recognition using PHOG and LPQ features

Abhinav Dhall*, Akshay Asthana, Roland Goecke, Tom Gedeon

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    200 Citations (Scopus)

    Abstract

    We propose a method for automatic emotion recognition as part of the FERA 2011 competition. The system extracts pyramid of histogram of gradients (PHOG) and local phase quantisation (LPQ) features for encoding the shape and appearance information. For selecting the key frames, K-means clustering is applied to the normalised shape vectors derived from constraint local model (CLM) based face tracking on the image sequences. Shape vectors closest to the cluster centers are then used to extract the shape and appearance features. We demonstrate the results on the SSPNET GEMEP-FERA dataset. It comprises of both person specific and person independent partitions. For emotion classification we use support vector machine (SVM) and largest margin nearest neighbour (LMNN) and compare our results to the pre-computed FERA 2011 emotion challenge baseline.

    Original languageEnglish
    Title of host publication2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011
    Pages878-883
    Number of pages6
    DOIs
    Publication statusPublished - 2011
    Event2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011 - Santa Barbara, CA, United States
    Duration: 21 Mar 201125 Mar 2011

    Publication series

    Name2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011

    Conference

    Conference2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011
    Country/TerritoryUnited States
    CitySanta Barbara, CA
    Period21/03/1125/03/11

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