Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis and Synthesis of Facial Image Sequences Using Physical and Anatomical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial Expression Recognition and Its Degree Estimation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Audio-visual emotion recognition in adult attachment interview
Proceedings of the 8th international conference on Multimodal interfaces
Japanese Face Emotions Classification Using LIP Features
GMAI '07 Proceedings of the Geometric Modelling and Imaging
Face detection and recognition of natural human emotion using Markov random fields
Personal and Ubiquitous Computing
Audio-Visual Emotion Recognition Using Gaussian Mixture Models for Face and Voice
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Human emotion detection is of substantial importance in diverse pervasive applications in assistive environments. Because facial expressions provide a key mechanism for understanding and conveying emotion, automatic emotion detection through facial expression recognition has attracted increased attention in both scientific research and practical applications in recent years. Traditional facial expression recognition methods normally use only one type of facial expression data, either static data extracted from one single face image or motion dependent data obtained from dynamic face image sequences, but seldom employ both. In this work, we propose a novel Discriminative Kernel Facial Emotion Recognition (DKFER) method to integrate these two types of facial expression data using a hybrid kernel, such that the advantages of both of them are exploited. In addition, by using Linear Discriminant Analysis (LDA) to transform the two types of original facial expression data into two more discriminative lower-dimensional subspaces, the succeeding classification for emotion detection can be carried out in a more efficient and effective way. Encouraging experimental results in empirical studies demonstrate the practical usage of the proposed DKFER method for emotion detection.