Sparse Kernel Reduced-Rank Regression for Bimodal Emotion Recognition From Facial Expression and Speech
2016 (English)In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 18, no 7, 1319-1329 p.Article in journal (Refereed) PublishedText
A novel bimodal emotion recognition approach from facial expression and speech based on the sparse kernel reduced-rank regression (SKRRR) fusion method is proposed in this paper. In this method, we use the openSMILE feature extractor and the scale invariant feature transform feature descriptor to respectively extract effective features from speech modality and facial expression modality, and then propose the SKRRR fusion approach to fuse the emotion features of two modalities. The proposed SKRRR method is a nonlinear extension of the traditional reduced-rank regression (RRR), where both predictor and response feature vectors in RRR are kernelized by being mapped onto two high-dimensional feature space via two nonlinear mappings, respectively. To solve the SKRRR problem, we propose a sparse representation (SR)-based approach to find the optimal solution of the coefficient matrices of SKRRR, where the introduction of the SR technique aims to fully consider the different contributions of training data samples to the derivation of optimal solution of SKRRR. Finally, we utilize the eNTERFACE '05 and AFEW4.0 bimodal emotion database to conduct the experiments of monomodal emotion recognition and bimodal emotion recognition, and the results indicate that our presented approach acquires the highest or comparable bimodal emotion recognition rate among some state-of-the-art approaches.
Place, publisher, year, edition, pages
2016. Vol. 18, no 7, 1319-1329 p.
Bimodal emotion recognition, facial expression, feature fusion, sparse kernel reduced-rank regression (SKRRR), speech
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-190497DOI: 10.1109/TMM.2016.2557721ISI: 000379752600008ScopusID: 2-s2.0-84976556360OAI: oai:DiVA.org:kth-190497DiVA: diva2:954041
QC 201608192016-08-192016-08-122016-08-19Bibliographically approved