Learning local binary patterns for gender classification on real-world face images
By Caifeng Shan. Pattern Recognition Letters, 2012
391 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/852ff0d410a25ebb7936043a05efe2469c699e4b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Abdelrahman Abdelhamed
- Mahmoud Afifi
- Tal Hassner
- Gil Levi
- Sen Jia
- Thomas Lansdall-Welfare
- Nello Cristianini
- Junsong Yuan
In Signal Processing
- Gil Levi
- Sen Jia
- Thomas Lansdall-Welfare
- Nello Cristianini
- Mahmoud Afifi
- Abdelrahman Abdelhamed
- Roberto Paredes Palacios
- Alberto Albiol
Citations in the corpus
- AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces (2017, background): “In literature, many gender classification methods are applied only to frontal or near-frontal face images [37, 38, 7, 39, 40].”
- Local Deep Neural Networks for gender recognition (2016, methodology): “More elaborated features include Haar-like wavelets (Shakhnarovich et al. (2002)), Local Binary Patterns (LBPs) (Shan (2012)) or Gabor wavelets (Leng and Wang (2008)).”
- Minimalistic CNN-based ensemble model for gender prediction from face images (2016, methodology): “Thus, the CNN I which is trained on only 10% of the initial data (i.e. on 45, 000 images) performs reasonably well: 95.98% of correct gender predictions on LFW (it is better than, for example, the model by Shan (2012) which was the state-of-the-art on LFW in 2012).”
- Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns (2015, background): “They have since been successfully applied to a wide range of other image recognition tasks, most notably face recognition [1, 41], age estimation [6] and gender classification [35].”
- Age and gender classification using convolutional neural networks (2015, methodology): “Before describing the proposed method we briefly review related methods for age and gender classification and provide a cursory overview of deep convolutional networks.”
- Face Recognition Vendor Test (FRVT) - Performance of Automated Gender Classification Algorithms (2015, neutral): “1 25 099 Shan (2010) [22] 77.”
- Learning LBP structure by maximizing the conditional mutual information (2015, background): “Potentially, the proposed approach could handle a large set of binarized pixel differences, whereas approaches that directly extract features from the histogram bins [3, 8, 31, 32, 43–45] cannot as it is not feasible to enumerate 2 bins for a large n.”
- Gender Classification by Deep Learning on Millions of Weakly Labelled Images (2016, background): “Authors Test set Features Classifiers Result Cross Shan [12] LFW(7,443) Boosted LBP SVM 94.”