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FGR
2011
IEEE

Emotion recognition using PHOG and LPQ features

10 years 3 months ago
Emotion recognition using PHOG and LPQ features
— We propose a method for automatic emotion recognition as part of the FERA 2011 competition [1] . 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, kmeans 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 GEMEPFERA dataset. It comprises of both person specific and person independent partitions. For emotion classification we use support vector machine (SVM) and largest margin nearest neighbor (LMNN) and compare our results to the pre-computed FERA 2011 emotion challenge baseline [1].
Abhinav Dhall, Akshay Asthana, Roland Goecke, Tom
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where FGR
Authors Abhinav Dhall, Akshay Asthana, Roland Goecke, Tom Gedeon
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