Proceedings ArticleDOI
Face recognition using scattering wavelet under Illicit Drug Abuse variations
Prateekshit Pandey,Richa Singh,Mayank Vatsa +2 more
- pp 1-6
TLDR
An autoencoder-style mapping function (AutoScat) is proposed that learns to encode the ScatNet representation of a face image to reduce the computation time.Abstract:
Prolonged usage of illicit drugs alter texture and geometric variations of a face and hence, affect the performance of face recognition algorithms. This research proposes a two fold contribution for advancing the state-of-art in recognizing face images with variations caused due to substance abuse: firstly, scattering transform (ScatNet) based face recognition algorithm is proposed. The algorithm yields good results however, it is very expensive in terms of the computational time and space. Therefore, as the next contribution, an autoencoder-style mapping function (AutoScat) is proposed that learns to encode the ScatNet representation of a face image to reduce the computation time. The results are evaluated on the publicly available Illicit Drug Abuse Face database. The results show that ScatNet based face recognition algorithm outperforms two commercial matchers. Further, compared with ScatNet, AutoScat is able to achieve lower rank-1 accuracy but requires 10−3 times lesser computational requirements and around 400 times smaller feature space.read more
Citations
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Journal ArticleDOI
Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering
TL;DR: This paper proposes the automatic method of classification using the Harmony-Crow Search (HCS) Optimization algorithm to train the multi-SVNN classifier to determine the level of the brain tumor using the features of the segments generated based on Bayesian fuzzy clustering.
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Optimization driven Deep Convolution Neural Network for brain tumor classification
TL;DR: This work introduces an optimized deep learning mechanism; named Dolphin-SCA based Deep CNN, to improve the accuracy and to make effective decisions in classification of brain tumor classification.
Journal ArticleDOI
A computer-aided diagnosis system for HEp-2 fluorescence intensity classification.
TL;DR: A computer-aided diagnosis (CAD) system classifying HEp-2 fluorescence intensity is presented, which represents each image using an Invariant Scattering Convolutional Network (Scatnet), which is locally translation invariant and stable to deformations, a characteristic useful in case of HEP-2 samples.
Journal ArticleDOI
Dermoscopic image classification using CNN with Handcrafted features
Kotra Sankar Raja Sekhar,Tummala Ranga Babu,Goriparthi Prathibha,Kotra Vijay,Long Chiau Ming +4 more
TL;DR: This work proposes a method to improve the accuracy for identifying Melanoma and different skin lesion classification when compared to the other state of the art methods, using dermoscopic images obtained from the International Skin Image Collaboration Archive 2016.
Journal ArticleDOI
Severity detection and infection level identification of tuberculosis using deep learning
R. S. Chithra,P. Jagatheeswari +1 more
TL;DR: The proposed AFC‐Deep CNN algorithm is designed by modifying FC algorithm using self‐adaptive concept and shows better performance with maximum accuracy value as 0.935.
References
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising 1 criterion
TL;DR: This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
Journal Article
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
TL;DR: Denoising autoencoders as mentioned in this paper are trained locally to denoise corrupted versions of their inputs, which is a straightforward variation on the stacking of ordinary autoencoder.
BookDOI
Handbook of Face Recognition
Stan Z. Li,Anil K. Jain +1 more
TL;DR: This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems, as well as offering challenges and future directions.
Journal ArticleDOI
Invariant Scattering Convolution Networks
Joan Bruna,Stéphane Mallat +1 more
TL;DR: The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification.
Proceedings ArticleDOI
Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination
Laurent Sifre,Stéphane Mallat +1 more
TL;DR: An affine invariant representation is constructed with a cascade of invariants, which preserves information for classification and state-of-the-art classification results are obtained over texture databases with uncontrolled viewing conditions.