The nature of statistical learning theory
The nature of statistical learning theory
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Normalization in Support Vector Machines
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Gender Classification with Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Journal of Cognitive Neuroscience
Classification of Faces in Man and Machine
Neural Computation
A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Fusing gait and face cues for human gender recognition
Neurocomputing
SODA-boosting and its application to gender recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Gender and ethnicity identification from silhouetted face profiles
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Extracting gender discriminating features from facial needle-maps
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Supervised relevance maps for increasing the distinctiveness of facial images
Pattern Recognition
Gender discriminating models from facial surface normals
Pattern Recognition
Can gender be predicted from near-infrared face images?
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
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This paper addresses the issue of combining pre-processing methods--dimensionality reduction using Principal Component Analysis (PCA) and Locally Linear Embedding (LLE)--with Support Vector Machine (SVM) classification for a behaviorally important task in humans: gender classification. A processed version of the MPI head database is used as stimulus set. First, summary statistics of the head database are studied. Subsequently the optimal parameters for LLE and the SVM are sought heuristically. These values are then used to compare the original face database with its processed counterpart and to assess the behavior of a SVM with respect to changes in illumination and perspective of the face images. Overall, PCA was superior in classification performance and allowed linear separability.