SexNet: A neural network identifies sex from human faces
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Learning Framework for Real Time Face Detection and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
A Local Region-based Approach to Gender Classi.cation From Face Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Boosting Sex Identification Performance
International Journal of Computer Vision
An Experimental Study on Automatic Face Gender Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Gender Recognition in Non Controlled Environments
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
FaceTracer: A Search Engine for Large Collections of Images with Faces
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Combining appearance and motion for face and gender recognition from videos
Pattern Recognition
Face Gender Classification on Consumer Images in a Multiethnic Environment
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Gender classification based on boosting local binary pattern
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Boosting local binary pattern (LBP)-Based face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Similarity scores based on background samples
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Understanding critical factors in appearance-based gender categorization
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
WaveLBP based hierarchical features for image classification
Pattern Recognition Letters
Gender Recognition Based On Combining Facial and Hair Features
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Gender Recognition Based On Combining Facial and Hair Features
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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Gender recognition is one of fundamental face analysis tasks. Most of the existing studies have focused on face images acquired under controlled conditions. However, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this paper, we investigate gender recognition on real-life faces using the recently built database, the Labeled Faces in the Wild (LFW). Local Binary Patterns (LBP) is employed to describe faces, and Adaboost is used to select the discriminative LBP features. We obtain the performance of 94.81% by applying Support Vector Machine (SVM) with the boosted LBP features. The public database used in this study makes future benchmark and evaluation possible.