AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces
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TLDR
In this paper, the combination of isolated facial components and a contextual feature called foggy face is used to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2019-07-01 and is currently open access. It has received 85 citations till now. The article focuses on the topics: AdaBoost & Feature (computer vision).read more
Citations
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Journal ArticleDOI
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
TL;DR: Visualization results demonstrate that, compared with the CNN without Gate Unit, ACNNs are capable of shifting the attention from the occluded patches to other related but unobstructed ones and outperform other state-of-the-art methods on several widely used in thelab facial expression datasets under the cross-dataset evaluation protocol.
Journal ArticleDOI
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis
TL;DR: It is found that the majority of image databases rarely contain underlying source material for how race and gender identities are defined and annotated, and that the lack of critical engagement with this nature renders databases opaque and less trustworthy.
Proceedings Article
The Price of Fair PCA: One Extra dimension
TL;DR: In this article, the authors investigate whether the standard dimensionality reduction technique of PCA inadvertently produces data representations with different fidelity for two different populations, and give a polynomial-time algorithm for finding a low dimensional representation of the data which is nearly-optimal with respect to this measure.
Journal ArticleDOI
11K Hands: Gender recognition and biometric identification using a large dataset of hand images
Mahmoud Afifi,Mahmoud Afifi +1 more
TL;DR: Zhang et al. as mentioned in this paper proposed a two-stream convolutional neural network (CNN) which accepts hand images as input and predicts gender information from these hand images, which is then used as a feature extractor to feed a set of support vector machine classifiers for biometric identification.
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Gender recognition and biometric identification using a large dataset of hand images.
TL;DR: In this article, a large dataset of human hand images with detailed ground-truth information for gender recognition and biometric identification is proposed, which includes 11,076 hand images (dorsal and palmar sides), from 190 subjects of different ages under the same lighting conditions.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.