Machine Learning
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
A Comparison of the Gender Differentiation Capability between Facial Parts
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Gender Recognition in Non Controlled Environments
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Learning Mixture Models for Gender Classification Based on Facial Surface Normals
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
The Role of Face Parts in Gender Recognition
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Gender identification using feature patch-based bayesian classifier
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
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This paper evaluates the expected complementarity between the most prominent parts of the face for the gender recognition task. Given the image of a face, five important parts (right and left eyes, nose, mouth and chin) are extracted and represented as appearance-based data vectors. In addition, the full face and its internal rectangular region (excluding hair, ears and contour) are also coded. Several mixtures of classifiers based on (subsets of) these five single parts were designed using simple voting, weighted voting and other learner as combiners. Experiments using the FERET database prove that ensembles perform significantly better than plain classifiers based on single parts (as expected).