Machine Learning
The FERET Evaluation Methodology for Face-Recognition Algorithms
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
Empirical Study of Multi-scale Filter Banks for Object Categorization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - 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
On the Complementarity of Face Parts for Gender Recognition
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Gender Recognition from a Partial View of the Face Using Local Feature Vectors
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Family facial patch resemblance extraction
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Gender discriminating models from facial surface normals
Pattern Recognition
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This paper evaluates the discriminant capabilities of face parts in gender recognition. Given the image of a face, a number of subimages containing the eyes, nose, mouth, chin, right eye, internal face (eyes, nose, mouth, chin), external face (hair, ears, contour) and the full face are extracted and represented as appearance-based data vectors. A greater number of face parts from two databases of face images (instead of only one) were considered with respect to previous related works, along with several classification rules. Experiments proved that single face parts offer enough information to allow discrimination between genders with recognition rates that can reach 86%, while classifiers based on the joint contribution of internal parts can achieve rates above 90%. The best result using the full face was similar to those reported in general papers of gender recognition (95%). A high degree of correlation was found among classifiers as regards their capacity to measure the relevance of face parts, but results were strongly dependent on the composition of the database. Finally, an evaluation of the complementarity between discriminant information from pairs of face parts reveals a high potential to define effective combinations of classifiers.