Age regression from soft aligned face images using low computational resources
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Gender classification via global-local features fusion
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
On the importance of multi-dimensional information in gender estimation from face images
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Face verification of age separated images under the influence of internal and external factors
Image and Vision Computing
Gender recognition using cognitive modeling
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Measuring the degree of face familiarity based on extended NMF
ACM Transactions on Applied Perception (TAP)
Robust gender recognition by exploiting facial attributes dependencies
Pattern Recognition Letters
| Hi-index | 0.14 |
Emerging applications of computer vision and pattern recognition in mobile devices and networked computing require the development of resource-limited algorithms. Linear classification techniques have an important role to play in this context, given their simplicity and low computational requirements. The paper reviews the state-of-the-art in gender classification, giving special attention to linear techniques and their relations. It discusses why linear techniques are not achieving competitive results and shows how to obtain state-of-the-art performances. Our work confirms previous results reporting very close classification accuracies for Support Vector Machines (SVMs) and boosting algorithms on single-database experiments. We have proven that Linear Discriminant Analysis on a linearly selected set of features also achieves similar accuracies. We perform cross-database experiments and prove that single database experiments were optimistically biased. If enough training data and computational resources are available, SVM's gender classifiers are superior to the rest. When computational resources are scarce but there is enough data, boosting or linear approaches are adequate. Finally, if training data and computational resources are very scarce, then the linear approach is the best choice.