Face recognition using cloud Hopfield neural network

Soni, Neha; Singh, Narotam; Kapoor, Amita; Sharma, Enakshi K.

doi:10.1109/wispnet.2016.7566167

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Some of the DLNs have 10 reported surpassing human-level accuracy in certain tasks. We explored a large number of research papers and books (Gulli 2017;Singh 2015;Krizhevsky 2012;Karpathy 2014;Simard 2003;Taigman 2014;He 2015;Vinyals 2015;Soni 2016;2018b;Bahdanau 2014;Sutskever 2014, Wen 2015Xiong 2017;Sak 2015;Amodei 2015;Chung 2011) (Krizhevsky 2012;Karpathy 2014;Simard 2003;Taigman 2014;He 2015;Vinyals 2015). For textual data, the two variants of gated recurrent neural networks (gated-RNNs) i.e.…”

Section: Systemsmentioning

confidence: 99%

Artificial Intelligence in Business: From Research and Innovation to Market Deployment

Soni

Sharma

Singh

et al. 2020

Procedia Computer Science

Self Cite

173

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The fast pace of artificial intelligence (AI) and automation is propelling strategists to reshape their business models. This is fostering the integration of AI in the business processes but the consequences of this adoption are underexplored and needs attention. This paper focuses on the overall impact of AI on businesses -from research, innovation, market deployment to future shifts in business models. To access this overall impact, we design a three dimensional research model, based upon the Neo-Schumpeterian economics and its three forces viz. innovation, knowledge, and entrepreneurship. The first dimension deals with research and innovation in AI. In the second dimension, we explore the influence of AI on the global market and the strategic objectives of the businesses and finally the third dimension examines how AI is shaping business contexts. Additionally, the paper explores AI implications on actors and its dark sides.

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Section: Systemsmentioning

confidence: 99%

Artificial Intelligence in Business: From Research and Innovation to Market Deployment

Soni

Sharma

Singh

et al. 2020

Procedia Computer Science

Self Cite

173

View full text Add to dashboard Cite

show abstract

“…Neurons in the HNN are also available in both continuous-valued representation (analog value) and discrete-valued representation (binary value). Experimental results have confirmed that the performance of a discrete HNN (DHNN), using asynchronous updates and discrete-valued representations, is superior to that of other HNNs [12,13].…”

Section: Discrete Hopfield Neural Networkmentioning

confidence: 77%

Design of Discrete Hopfield Neural Network Using a Single Flux Quantum Circuit

Yamanashi

Yoshikawa

2022

IEEE Trans. Appl. Supercond.

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The superconductor single flux quantum (SFQ) logic family has been recognized as a promising candidate to resolve the energy consumption crisis in the post-Moore era, owing to its high switching speed and low power consumption. In the field of machine learning, where technology and computational requirements are growing rapidly (e.g., image recognition and natural language processing), there is great potential for the implementation of SFQ circuits. In this study, we investigate and implement a discrete Hopfield neural network (DHNN) using SFQ circuits. A DHNN is a binary neural network with less information than a standard full precision neural network; it also provides a higher processing speed. It is mainly used for pattern recognition and recovery. We designed the DHNN circuit with two patterns, each with eight elements. The circuit operates at the clock frequency of more than 50 GHz.

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“…Several approaches, such as holistic, local, and hybrid are developed for providing face image description with only fewer face image features or the whole facial features [5]. Numerous researchers have shown a better recognition rate for face recognition [6][7][8]. However, during the biometric validation, the face is affected by the illumination with several intensities of the light and different angles [9].…”

Section: Introductionmentioning

confidence: 99%

Novel BSSSO-Based Deep Convolutional Neural Network for Face Recognition with Multiple Disturbing Environments

2021

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Face recognition technology is presenting exciting opportunities, but its performance gets degraded because of several factors, like pose variation, partial occlusion, expression, illumination, biased data, etc. This paper proposes a novel bird search-based shuffled shepherd optimization algorithm (BSSSO), a meta-heuristic technique motivated by the intuition of animals and the social behavior of birds, for improving the performance of face recognition. The main intention behind the research is to establish an optimization-driven deep learning approach for recognizing face images with multiple disturbing environments. The developed model undergoes three main steps, namely, (a) Noise Removal, (b) Feature Extraction, and (c) Recognition. For the removal of noise, a type II fuzzy system and cuckoo search optimization algorithm (T2FCS) is used. The feature extraction is carried out using the CNN, and landmark enabled 3D morphable model (L3DMM) is utilized to efficiently fit a 3D face from a single uncontrolled image. The obtained features are subjected to Deep CNN for face recognition, wherein the training is performed using novel BSSSO. The experimental findings on standard datasets (LFW, UMB-DB, Extended Yale B database) prove the ability of the proposed model over the existing face recognition approaches.

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Face recognition using cloud Hopfield neural network

Cited by 5 publications

References 14 publications

Artificial Intelligence in Business: From Research and Innovation to Market Deployment

Artificial Intelligence in Business: From Research and Innovation to Market Deployment

Design of Discrete Hopfield Neural Network Using a Single Flux Quantum Circuit

Novel BSSSO-Based Deep Convolutional Neural Network for Face Recognition with Multiple Disturbing Environments

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