Membership authentication in the dynamic group by face classification using SVM ensemble
Pattern Recognition Letters
Applications of Support Vector Machines for Pattern Recognition: A Survey
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Support Vector Learning for Gender Classification Using Audio and Visual Cues: A Comparison
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition
International Journal of Computer Vision
Appearance-based gender classification with Gaussian processes
Pattern Recognition Letters
EURASIP Journal on Applied Signal Processing
A Framework for Multi-view Gender Classification
Neural Information Processing
Face Gender Classification on Consumer Images in a Multiethnic Environment
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
A novel Bayesian learning method for information aggregation in modular neural networks
Expert Systems with Applications: An International Journal
Logistic ensembles of Random Spherical Linear Oracles for microarray classification
International Journal of Data Mining and Bioinformatics
Facial gender classification using shape-from-shading
Image and Vision Computing
SODA-boosting and its application to gender recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Spatial Gaussian mixture model for gender recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Gender and ethnicity identification from silhouetted face profiles
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques
International Journal of Computer Vision
Neuro-fuzzy-combiner: an effective multiple classifier system
International Journal of Knowledge Engineering and Soft Data Paradigms
Proceedings of the international conference on Multimedia
Supervised relevance maps for increasing the distinctiveness of facial images
Pattern Recognition
Bag of soft biometrics for person identification
Multimedia Tools and Applications
Gender classification by principal component analysis and support vector machine
Proceedings of the 2011 International Conference on Communication, Computing & Security
Kinship verification from facial images under uncontrolled conditions
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Multimodal facial gender and ethnicity identification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Investigating the impact of face categorization on recognition performance
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Remote sensing image classification: a neuro-fuzzy MCS approach
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Soft biometric classification using local appearance periocular region features
Pattern Recognition
Improving gender recognition using genetic algorithms
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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We describe the application of mixtures of experts on gender and ethnic classification of human faces, and pose classification, and show their feasibility on the FERET database of facial images. The mixture of experts is implemented using the “divide and conquer” modularity principle with respect to the granularity and/or the locality of information. The mixture of experts consists of ensembles of radial basis functions (RBFs). Inductive decision trees (DTs) and support vector machines (SVMs) implement the “gating network” components for deciding which of the experts should be used to determine the classification output and to restrict the support of the input space. Both the ensemble of RBF's (ERBF) and SVM use the RBF kernel (“expert”) for gating the inputs. Our experimental results yield an average accuracy rate of 96% on gender classification and 92% on ethnic classification using the ERBF/DT approach from frontal face images, while the SVM yield 100% on pose classification