Learning Gender with Support Faces

By Baback Moghaddam, Ming-Hsuan Yang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

662 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b14d3375cb9c92e04a8bd9176bbaa4f536f29461

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Citations in the corpus

Age and Gender Estimation of Unfiltered Faces (2014, methodology): “Face image intensities were directly classified using SVM in [43] and later again using AdaBoost in [44].”
Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks (2008, background): “The second approach has demonstrated state-of-the-art accuracy for gender recognition [3,17].”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, background): “In addition to the aforementioned challenges relating to the perceptual idiosyncracies of humans, there are also challenges relating to the limitations of algorithmic and hardware resources.”
Local Deep Neural Networks for gender recognition (2016, background): “The handcrafted features found in the literature for gender recognition can be as simple as the raw pixels (Moghaddam and Yang (2002)) or pixel differences (Baluja and Rowley (2007)).”
Boosting Sex Identification Performance (2005, methodology): “Shakhnarovich et al. (2002) applied AdaBoost with the features used by the face detection system of Viola and Jones (2001). In those experiments, over 3500 images were collected from the web, and a face detector was used to find the face images.”
Age and gender classification using convolutional neural networks (2015, methodology): “SVM classifiers were used by [35], applied directly to image intensities.”
Gait Components and Their Application to Gender Recognition (2008, methodology): “The SVM is also used in [28] for gender recognition based on face images.”
Attribute And-Or Grammar for Joint Parsing of Human Attributes, Part and Pose (2016, background): “Early work focused on facial images since face is the most informative and distinct part of the body, and is the most suitable for estimating attributes such as gender [22], [23], [24], age [25], and some local attributes (e.”
Support Vector Machines with Applications (2006, methodology): “Concerning face recognition, gender detection has been analyzed by Moghaddam and Yang [45].”
More generality in efficient multiple kernel learning (2009, methodology): “It is demonstrated that, on the benchmark database of (Moghaddam & Yang, 2002), GMKL can outperform MKL by as much as 10% for a given number of features.”
Learning local binary patterns for gender classification on real-world face images (2012, methodology): “Moghaddam and Yang (2002) used raw image pixels with nonlinear SVMs for gender classiﬁcation on thumbnail faces (12 (cid:2) 21 pixels); their experiments on the FERET database (1,755 faces) demonstrated SVMs are superior to other classiﬁers, achieving the accuracy of 96.6%.”
Describing people: A poselet-based approach to attribute classification (2011, background): “Comparison with other methods is challenging because the vast majority of person-specific attribute classification methods operate on frontal faces only [19, 24, 25].”
Describable Visual Attributes for Face Verification and Image Search (2011, unknown): “and Yang [36] used Support Vector Machines (SVMs) [11]”