EMPATH: face, emotion, and gender recognition using holons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Gender Classification with Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Study on Automatic Face Gender Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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
| Hi-index | 0.00 |
In this paper we propose a new tensor based analysis algorithm for face gender recognition, in which we consider the different feature structures of male/female images respectively Given a gender labeled face dataset, we aim to obtain their meaningful low-dimensional data representation which preserves their intrinsic male/female structures, and this is achieved by combining tensor analysis with a local geometric preserving constraint on the tensor decomposition In the proposed approach, a similarity graph is built to represent images of the same gender and separate those of different genders Technically, a 5-mode (w.r.t gender, pose, illumination, expression, pixels) tensor decomposition is used to analyze the packed image matrix, which is constrained on the proposed graph and this graph can preserve as much as possible on the information of gender in the decomposed component data The objective of gender recognition is formulated as an optimization problem and then solved by an alternating algorithm Finally, experiments are implemented on several face databases and it is proved that the proposed approach can enhance gender discriminant capability significantly compared to the tensor approach, while has already achieved a comparable recognition performance as a state-of-art algorithm.