Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection Using Improved LBP under Bayesian Framework
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A LBP-based Face Recognition Method with Hamming Distance Constraint
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
Dynamic texture recognition using volume local binary patterns
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Combining contrast information and local binary patterns for gender classification
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Multimodal facial gender and ethnicity identification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Multi-view gender classification using local binary patterns and support vector machines
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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RGB-D is a powerful source of data providing the aligned depth information which has great potentials in improving the performance of various problems in image understanding, while Local Binary Patterns (LBP) have shown excellent results in representing faces. In this paper, we propose a novel efficient LBP-based descriptor, namely Gradient-LBP (G-LBP), specialized to encode the facial depth information inspired by 3DLBP, yet resolves its inherent drawbacks. The proposed descriptor is applied to gender recognition task and shows its superiority to 3DLBP in all the experimental setups on both Kinect and range scanner databases. Furthermore, a weighted combination scheme of the proposed descriptor for depth images and the state-of-the-art LBPU2 for grayscale images applied in gender recognition is proposed and evaluated. The result reinforces the effectiveness of the proposed descriptor in complementing the source of information from the luminous intensity. All the experiments are carried out on both the high quality 3D range scanner database - Texas 3DFR and images of lower quality obtained from Kinect - EURECOM Kinect Face Dataset to show the consistency of the performance on different sources of RGB-D data.