A deep architecture for face recognition based on multiple feature extraction techniques

Albelwi, Saleh; Mahmood, Ausif

doi:10.1109/icsipa.2017.8120642

Cited by 5 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Convolution layers are trained using labeled data of desired classes to extract desired features from a given input signal. After training, the network learns local features to map a given input signal to its closest output class by minimizing a loss function (Albelwi & Mahmood 2017).…”

Section: Modulating Signals Of Extra-terrestrial Originalmentioning

confidence: 99%

Opportunities to search for extraterrestrial intelligence with the FAST

Gajjar

Wang

et al. 2020

Res. Astron. Astrophys.

View full text Add to dashboard Cite

The discovery of ubiquitous habitable extrasolar planets, combined with revolutionary advances in instrumentation and observational capabilities, has ushered in a renaissance in the search for extra-terrestrial intelligence (SETI). Large scale SETI activities are now underway at numerous international facilities. The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the largest single-aperture radio telescope in the world, well positioned to conduct sensitive searches for radio emission indicative of exo-intelligence. SETI is one of the five key science goals specified in the original FAST project plan. A collaboration with the Breakthrough Listen Initiative has been initiated in 2016 with a joint statement signed both by Dr. Jun Yan, the then director of the National Astronomical Observatories, Chinese Academy of Sciences (NAOC), and Dr. Peter Worden, the Chairman of the Breakthrough Prize Foundation. In this paper, we highlight some of the unique features of FAST that will allow for novel SETI observations. We identify and describe three different signal types indicative of a technological source, namely, narrow-band, wide-band artificially dispersed, and modulated signals. We here propose observations with FAST to achieve sensitivities never before explored.

show abstract

Section: Modulating Signals Of Extra-terrestrial Originalmentioning

confidence: 99%

Opportunities to search for extraterrestrial intelligence with the FAST

Gajjar

Wang

et al. 2020

Res. Astron. Astrophys.

View full text Add to dashboard Cite

show abstract

“…Aleh Albelwi [4] developed a new technique by using deep learning to improve the performance of face recognition technique. Narayanan T [5] developed an algorithm with the help of principal component analysis and feed forward neural network to increase the recognition accuracy.…”

Section: Feature Extractionmentioning

confidence: 99%

Image Analysis using Non Negative Matrix Factorization

Prasad*,

Kumar,

Suresh

2020

IJRTE

View full text Add to dashboard Cite

Image analysis extracts the meaningful information from the images. This information is very much helpful to recognition and authentication. There are number of techniques available for image analysis. Image analysis can be used in analysis of the scene, image understanding and computer vision. Image analysis can be used in Medical image processing, Geology, optical character recognition and forensics. There are mainly four steps in the image analysis 1.image pre processing 2. Segmentation 3.feature extraction and 4. Classification and interpretation. Feature extraction is the main part for any image analysis. In this paper Multi modal biometric authentication system can be defined for security. In this process Non Negative Matrix Factorization (NMF) technique is used for feature extraction and for fusion Principle component analysis is used. After getting the features these can be encoding using Kronecker product. At the end Euclidean distance measure is used for authentication.

show abstract

“…The proposed results additionally expose that this technique offers 8% better outcome in Depth category recognition while almost 2% in depth instance recognition when compared to HMP on 70 30 folds of training and testing out, respectively. aleh Albelwi [5] proposed technique to improve the accuracy of face recognition technique. The author proposes the integration of multiple extraction features with the deep learning approach.…”

Section: Image Recognition and Feature Extraction: Background And mentioning

confidence: 99%

A Review Paper on Feature Extraction and General Approaches for Face Recognition

Bansal¹

2018

IJRASET

View full text Add to dashboard Cite

To recognize a face is itself an important aspect of visual perception. Face recognition is a combination of bottom-up and top-down processing. Many different approaches used in computer vision should parallel the range of ways leading to face recognition by humans, using different sources of observations. Feature extraction can be used for making face recognition more efficient. In this paper, a review of different approaches used for feature extraction techniques and face recognition based on a different method is done.

show abstract

A deep architecture for face recognition based on multiple feature extraction techniques

Cited by 5 publications

References 21 publications

Opportunities to search for extraterrestrial intelligence with the FAST

Opportunities to search for extraterrestrial intelligence with the FAST

Image Analysis using Non Negative Matrix Factorization

A Review Paper on Feature Extraction and General Approaches for Face Recognition

Contact Info

Product

Resources

About