[PDF] Top 20 Face Authentication Using Efficient Deep Convolutional Neural Network
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Face Authentication Using Efficient Deep Convolutional Neural Network
... Recently, face recognition significantly becomes a vital research scope and there are several extensive works devoted to leverage and advance face recognition techniques [1, 2, 3, ...applications ... See full document
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Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... degrade face recognition performance. Inspired by the success of deep convolutional neural networks (DCNN) on super resolution, in this paper, a method based on deep ... See full document
8
Robust Face Recognition Based on Convolutional Neural Network
... on deep learning [1, 2] has achieved a series of breakthroughs in recent ...4.4M face image and firstly used it for face verification ...[1] network, designed with the introduction of triplet ... See full document
6
Integrated Animal Recognition and Detection Using Deep Convolutional Neural Network
... of using a quadtree to hierarchically represent the image data for convolutional neural ...the network even when repeated convolution layers are stacked, hence retaining the sparse nature of ... See full document
7
1. Efficient face recognition using convolutional neural networks
... in Neural Information Processing Systems ...person authentication are suggested in [11,12,14,15] ,The best learning algorithms using convolutional networks are presented in Lecun ... See full document
7
Automated Detection of Gender from Face Images
... the face region, Deep Learning - Convolutional Neural Network and Deep Learning - Tensor ...tensors. Convolutional Neural Network (CNN) as one of the most ... See full document
5
Fruit Recognition Using Deep Convolutional Neural Network With Color Feature
... Convolution Neural Network is one kind of artificial network, which is helpful in image identification, classification, extraction and processing the ...Artificial Neural Networks uses ... See full document
5
Developing A Face Recognition System Using Convolutional Neural Network
... and authentication share the same preprocessing and feature extraction steps ...A face identification is a technique to identify or verify a person by his physical characteristic or personal unique traits, ... See full document
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Different Attack Patterns For Deep Brain Implants By Using Cnn
... Abstract: Deep Brain Stimulation (DBS) is a neuro surgical procedure that is a neuro stimulator (brain pacemaker) is placed which can send the electrical impulses, through implanted electrodes, to specific targets ... See full document
5
Face recognition with Bayesian convolutional networks for robust surveillance systems
... 2.2 Deep learning-based face recognition approaches Although machine learning techniques for facial recogni- tion have provided decent results, these techniques do not perform well under unconstrained ... See full document
10
Smart Ship Detection using Transfer Learning with ResNet
... that efficient, changing the parameters again and again with different images and all can be a time consuming and tedious ...semi-automatic, Deep learning. Deep learning together with computer vision ... See full document
5
Semi-supervised adversarial variational autoencoder
... for deep features such as style reconstruction loss (Gatys ...for deep features, contrary to [56] and Hou [54] who used the pretrained deep convolutional neural network VGGNet ... See full document
17
Phonocardiographic sensing using deep learning for abnormal heartbeat detection
... approach using RNNs for pathological heart murmurs detection provides almost 100% classification ...presented using three metrics: sensi- tivity, specificity, and challenge scores ...computationally ... See full document
8
Human Face Recognition in Video using Convolutional Neural Network (CNN)
... point. To do so, these points must be searched across the image and scale dimensions using a saliency criterion. The search through scale space is fundamental to achieve scale invariance and it is realized in ... See full document
7
Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks
... Magnetic resonance imaging is an advanced medical imaging technique providing rich information about the human soft tissue anatomy [5]. Automatic brain tumour detection from magnetic resonance images (MRIs) aims to ... See full document
10
A general purpose intelligent surveillance system for mobile devices using deep learning
... the network is training then a speed increase is achieved as no time will be spent building the batches in between ...first convolutional layer which convolves patches of the image of size 11x11 with stride ... See full document
8
Probabilistic Spatial Regression using a Deep Fully Convolutional Neural Network
... the images was manually annotated for the vertebral boundaries and centers by expert radio- graphers. Two examples with the corresponding manual annotations are shown in Fig. 1a. The corner point of a cervical vertebra ... See full document
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A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images
... There are several methods in the literature performing the TKV estimation; traditional methodologies, requir- ing imaging acquisitions, such as Computed Tomography (CT) and Magnetic Resonance (MR), include stere- ology ... See full document
12
A CNN Based Approach for Garments Texture Design Classification
... some deep learning techniques for garments design classification. Deep Network learn features automatically from large number of unlabelled data, hence more useful hidden discriminative features are ... See full document
7
The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Di erent Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review
... The heterogeneity of the included studies was a limitation of this review, since this prevented us from performing a meta-analysis to statistically compare the performance of deep learning algorithms. Thus, our ... See full document
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