face recognition using deep learning
Deep face recognition .
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For the YTF videos, K face descriptors are obtained for each video by ordering the faces by their facial landmark confidence score, and selecting the top K. Frontal faces are Page 9. PARKHI et al.: DEEP FACE RECOGNITION 9 No. Config Data Train Align
Deep learning with COTS HPC systems
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models in the literature are now top performers in supervised vi- sual recognition tasks (Krizhevsky MPI provides a standard message passing inter- face that allows multiple processes in a cluster to unlabeled data can learn to iden- tify objects, such as human faces , in images
Openface: A general-purpose face recognition library with mobile applications
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Likewise, recognizing people is a primitive operation in mobile computing that adds context to applications such as cognitive assistance, social events, speaker Given an input image with multiple faces , face recognition systems typically first run face detection to isolate
Deep machine learning-a new frontier in artificial intelligence research
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Finally, recent developments in facial recognition show equivalent perfor- mance relative to humans in their ability to match query images against large numbers of Computer Vision and Pattern Recognition Conf B. Kwolek, Face detection using convolutional neu- ral
Deep learning using support vector machines
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We demonstrate superior perfor- mance on MNIST, CIFAR-10, and on a recent Kag- gle competition on recognizing face expressions 3. Experiments 3.1. Facial Expression Recognition The data consist of 28,709 48×48 images of faces under 7 different types of expression
Deep learning of invariant features via simulated fixations in video
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Abstract We apply salient feature detection and tracking in videos to simulate fixations and smooth pursuit in human vision Table 4: Acc. PubFig faces Method Acc PubFig [35] is a face recognition dataset with 58,797 images of 200 persons
Audio Replay Attack Detection with Deep Learning Frameworks.
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Integrated recognition , localization and detection using convolutional networks, arXiv [16] XWX, R. He, Z. Sun, and T. Tan, A light cnn for deep face representation with noisy
Accurate and robust face recognition from RGB-D images with a deep learning approach.
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Face recognition from RGB-D images utilizes 2 complementary types of image data, ie colour and depth images, to achieve more accurate recognition . In this paper, we propose a face recognition system based on deep learning , which can be used to verify and identify a
Siamese neural networks for one-shot image recognition
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state-of-the-art performance in a variety of applications such as web search, spam detection , caption generation, and speech and image recognition to transformation in the input space, usually by leveraging a model with many parameters and then using a large
Automatically recognizing facial expression: Predicting engagement and frustration
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CERT successfully tracked faces across a large majority of the validation corpus (mean=76% of frames While hand-to- face gestures present a significant complication in naturalistic tutoring data, there has 3D Constrained Local Model for Rigid and Non-Rigid Facial Tracking The chapter contains an interesting discussion on the most important factors that contribute to the interclass variation of human faces , such as lighting direction and facial expression, that often complicate the design of face recognition systems
Kinship verification with deep convolutional neural networks
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MF Dal Martello and LT Maloney. Lateralization of kin recognition signals in the human face . Kin recognition signals in adult faces . Vision Research, 49(1):38 43, 2009 Like father, like son: Facial expression dy- namics for kinship verification
A deep semi-nmf model for learning hidden representations
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stem from the difference in the appearance of the subjects, but also from other attributes, such as the pose of the head in relation to the camera, or the facial expres- sion In addition faces compromise of mainly hierarchical features and thus face clustering problems
A deep learning approach for subject independent emotion recognition from facial expressions
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200-205. [15] M. Matsugu, K. Mori, Y. Mitari, Y. Kaneda, Subject Independent Facial Expression Recognition with Robust Face Detection Using a Convolutional Neural Network, Neural Networks, Vol. 16, 2003, pp. 555-559
Face recognition across time lapse using convolutional neural networks
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and that a single statistical model cannot be applied to all faces since the [10] have proposed a discriminative approach based on the observation that facial appearance changes in face descriptors using CNN rather than hand-crafted features as current face aging methods do
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework.
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corresponds to the original VAE framework (Kingma Welling, 2014; Rezende et al., 2014) learning on a number of benchmark datasets, such as CelebA (Liu et al., 2015), chairs (Aubry et al., 2014) and faces using qualitative evaluation
Face recognition using neural network: a review
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The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is is done by comparing the characteristics of the new face to that of known faces . It has Face localization part, where mouth end point and eyeballs will be obtained
Deep attribute networks
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and 10 patches were generated: 9 patches were cropped by counting each facial landmark as that the DAN can effectively learn attribute scores from target objects, including human faces of deep learning in attribute-based classi- fication on unconstrained face verification and
Multiresolution deep belief networks
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It is created by merging 30 pre-existing databases and is one of the largest collection of face images It is worth noting the facial de- tails MrDBN captures (f) Λ Figure 2: Filters and residual variances of all three resolutions of MrDBN trained on faces . 0.001 for the top layer
Learning high-level concepts by training a deep network on eye fixations
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It is often used in image recognition models to obtain a more compact representation that in the third-layer are able to learn high-level concepts like faces , texts, windows and C. Koch, Predicting human gaze using low-level saliency combined with face detection , Advances in