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Deep Neural Networks Under
(IEEENew York, 2016)
Deep Texture Features for Robust Face Spoofing Detection
(2017-12-01)
Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits ...
DEEP FEATURES EXTRACTION FOR ROBUST FINGERPRINT SPOOFING ATTACK DETECTION
(Sciendo, 2019-01-01)
Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors ...
ResNet18 Supported Inspection of Tuberculosis in Chest Radiographs With Integrated Deep, LBP, and DWT Features
The lung is a vital organ in human physiology and disease in lung causes various health issues. The acute disease in lung is a medical emergency and hence several methods are developed and implemented to detect the lung ...
On the Learning of Deep Local Features for Robust Face Spoofing Detection
(2019-01-15)
Biometrics emerged as a robust solution for security systems. However, given the dissemination of biometric applications, criminals are developing techniques to circumvent them by simulating physical or behavioral traits ...
Ocular Recognition Using Deep Features for Identity Authentication
(2020-07-01)
Recently, ocular biometrics has been gaining importance in Biometrics due to the poor performance obtained in some cases by biometric systems based on characteristics of the whole face. This paper presents a new method for ...
Ocular Recognition Using Deep Features for Identity Authentication
(Ieee, 2020-01-01)
Recently, ocular biometrics has been gaining importance in Biometrics due to the poor performance obtained in some cases by biometric systems based on characteristics of the whole face. This paper presents a new method for ...
Towards vegetation species discrimination by using data-driven descriptors
(Ieee, 2016-01-01)
In this paper, we analyse the use of Convolutional Neural Networks (CNNs or ConvNets) to discriminate vegetation species with few labelled samples. To the best of our knowledge, this is the first work dedicated to the ...
Towards vegetation species discrimination by using data-driven descriptors
(2017-02-28)
In this paper, we analyse the use of Convolutional Neural Networks (CNNs or ConvNets) to discriminate vegetation species with few labelled samples. To the best of our knowledge, this is the first work dedicated to the ...
A tutorial review on entropy-based handcrafted feature extraction for information fusion
(2018-05-01)
Entropy (H) is the main subject of this article, concisely written to serve as a tutorial introducing two feature extraction (FE) methods for usage in digital signal processing (DSP) and pattern recognition (PR). The theory, ...