Name
Title | ||
|---|---|---|
Facial expression recognition algorithm based on feature fusion adaptive weighted HLAC |
Abstract | ||
|---|---|---|
In this paper, a novel method of facial expression recognition based on adaptive weighted higher-order local autocorrelation coefficient (WHLAC) is presented. Firstly, The method gets whole face region and sub-regions of eyebrows, eyes, nose and mouth. Secondly, the global features of the face regions and local features of the sub-regions are extracted by HLAC, the weights of sub-regions are calculated by fisher linear discriminant (FLD), and then these two parts features are fused together by the weights. Finally, the fused features are classified by FLD. The experimental results show that the proposed method has higher recognition rate and lower computational cost than Gabor, WLBP, HLAC for facial expression recognition. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SII.2013.6776616 | SII |
Keywords | Field | DocType |
higher-order local autocorrelation coefficient,facial expression recognition,face recognition,whlac,subregion extraction,statistical analysis,fld,feature fusion adaptive weighted hlac,global feature,feature extraction,local feature,fisher linear discriminant,face region | Computer vision,Facial recognition system,Feature fusion,Facial expression recognition,Pattern recognition,Feature extraction,Feature (machine learning),Artificial intelligence,Engineering,Autocorrelation coefficient,Linear discriminant analysis,Statistical analysis | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||