Emotion recognition using PHOG and LPQ features

Author

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

Author_Institution

Sch. of Comput. Sci., Australian Nat. Univ., Canberra, ACT, Australia

fYear

2011

fDate

21-25 March 2011

Firstpage

878

Lastpage

883

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

emotion recognition; feature extraction; image classification; image sequences; pattern clustering; support vector machines; LPQ features; PHOG; SSPNET GEMEP-FERA dataset; appearance features extraction; automatic emotion recognition; constraint local model; emotion classification; face tracking; image sequences; k-means clustering; largest margin nearest neighbour; local phase quantisation features; pyramid of histogram of gradient extraction; shape vectors; support vector machine; Accuracy; Databases; Face; Feature extraction; Image sequences; Shape; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Santa Barbara, CA

Print_ISBN

978-1-4244-9140-7

Type

conf

DOI

10.1109/FG.2011.5771366

Filename

5771366