Emotion Recognition Using PHOG and LPQ features

Abhinav Dhall; Akshay Asthana; Roland Goecke; Tamas Gedeon

doi:10.1109/FG.2011.5771366

Emotion Recognition Using PHOG and LPQ features

Abhinav Dhall, Akshay Asthana, Roland Goecke, Tamas Gedeon

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

200 Citations (Scopus)

4 Downloads (Pure)

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 language	English
Title of host publication	2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop
Editors	Kevin Bowyer, Marian Bartlett, Rainer Stiefelhagen
Place of Publication	Santa Barbara
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	878-883
Number of pages	6
ISBN (Electronic)	9781424491407
ISBN (Print)	9781424491414
DOIs	https://doi.org/10.1109/FG.2011.5771366
Publication status	Published - 2011
Event	2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011) - Santa Barbara, Santa Barbara, United States Duration: 21 Mar 2011 → 25 Mar 2011

Conference

Conference	2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011)
Country/Territory	United States
City	Santa Barbara
Period	21/03/11 → 25/03/11

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10.1109/FG.2011.5771366

Cite this

@inproceedings{1dbcae0a946043b3a1cd4c533e7dc083,

title = "Emotion Recognition Using PHOG and LPQ features",

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",

keywords = "Facial expression recognition, Emotion recognition",

author = "Abhinav Dhall and Akshay Asthana and Roland Goecke and Tamas Gedeon",

year = "2011",

doi = "10.1109/FG.2011.5771366",

language = "English",

isbn = "9781424491414",

pages = "878--883",

editor = "Kevin Bowyer and Marian Bartlett and Rainer Stiefelhagen",

booktitle = "2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = " 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011) ; Conference date: 21-03-2011 Through 25-03-2011",

}

Dhall, A, Asthana, A, Goecke, R & Gedeon, T 2011, Emotion Recognition Using PHOG and LPQ features. in K Bowyer, M Bartlett & R Stiefelhagen (eds), 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop. IEEE, Institute of Electrical and Electronics Engineers, Santa Barbara, pp. 878-883, 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011), Santa Barbara, California, United States, 21/03/11. https://doi.org/10.1109/FG.2011.5771366

Emotion Recognition Using PHOG and LPQ features. / Dhall, Abhinav; Asthana, Akshay; Goecke, Roland et al.
2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop. ed. / Kevin Bowyer; Marian Bartlett; Rainer Stiefelhagen. Santa Barbara: IEEE, Institute of Electrical and Electronics Engineers, 2011. p. 878-883.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Emotion Recognition Using PHOG and LPQ features

AU - Dhall, Abhinav

AU - Asthana, Akshay

AU - Goecke, Roland

AU - Gedeon, Tamas

PY - 2011

Y1 - 2011

N2 - 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

AB - 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

KW - Facial expression recognition

KW - Emotion recognition

U2 - 10.1109/FG.2011.5771366

DO - 10.1109/FG.2011.5771366

M3 - Conference contribution

SN - 9781424491414

SP - 878

EP - 883

BT - 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop

A2 - Bowyer, Kevin

A2 - Bartlett, Marian

A2 - Stiefelhagen, Rainer

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Santa Barbara

T2 - 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011)

Y2 - 21 March 2011 through 25 March 2011

ER -

Emotion Recognition Using PHOG and LPQ features

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