A robust feature extraction method based on monogenic filter for iris recognition system

2014 ◽  
Author(s):  
Walid Aydi ◽  
Nade Fadhel ◽  
Nouri Masmoudi ◽  
Lotfi Kamoun
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Iris Recognition ◽  
Recognition System ◽  
Feature Extraction Method ◽  
Robust Feature Extraction

scholarly journals A Robust Feature Extraction Method for Real-Time Speech Recognition System on a Raspberry Pi 3 Board

2019 ◽  
Vol 9 (2) ◽  
pp. 4066-4070 ◽  
Author(s):  
A. Mnassri ◽  
M. Bennasr ◽  
C. Adnane
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Real Time ◽  
Extraction Method ◽  
Recognition System ◽  
Raspberry Pi ◽  
Discrete Wavelet ◽  
Speech Recognition System ◽  
Feature Extraction Method ◽  
Robust Feature Extraction

The development of a real-time automatic speech recognition system (ASR) better adapted to environmental variabilities, such as noisy surroundings, speaker variations and accents has become a high priority. Robustness is required, and it can be performed at the feature extraction stage which avoids the need for other pre-processing steps. In this paper, a new robust feature extraction method for real-time ASR system is presented. A combination of Mel-frequency cepstral coefficients (MFCC) and discrete wavelet transform (DWT) is proposed. This hybrid system can conserve more extracted speech features which tend to be invariant to noise. The main idea is to extract MFCC features by denoising the obtained coefficients in the wavelet domain by using a median filter (MF). The proposed system has been implemented on Raspberry Pi 3 which is a suitable platform for real-time requirements. The experiments showed a high recognition rate (100%) in clean environment and satisfying results (ranging from 80% to 100%) in noisy environments at different signal to noise ratios (SNRs).

scholarly journals A Multiscale Chaotic Feature Extraction Method for Speaker Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jiang Lin ◽  
Yi Yumei ◽  
Zhang Maosheng ◽  
Chen Defeng ◽  
Wang Chao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Extraction Method ◽  
State Of The Art ◽  
Recognition System ◽  
Nonlinear Dynamic Model ◽  
Feature Extraction Method ◽  
Analysis Technique ◽  
Recognition Systems ◽  
Environment Noise

In speaker recognition systems, feature extraction is a challenging task under environment noise conditions. To improve the robustness of the feature, we proposed a multiscale chaotic feature for speaker recognition. We use a multiresolution analysis technique to capture more finer information on different speakers in the frequency domain. Then, we extracted the speech chaotic characteristics based on the nonlinear dynamic model, which helps to improve the discrimination of features. Finally, we use a GMM-UBM model to develop a speaker recognition system. Our experimental results verified its good performance. Under clean speech and noise speech conditions, the ERR value of our method is reduced by 13.94% and 26.5% compared with the state-of-the-art method, respectively.

scholarly journals A Novel and Efficient Feature Extraction Method for Iris Recognition

ETRI Journal ◽  
2007 ◽  
Vol 29 (3) ◽  
pp. 399-401 ◽  
Author(s):  
Jong-Gook Ko ◽  
Youn-Hee Gil ◽  
Jang-Hee Yoo ◽  
Kyo-IL Chung
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Iris Recognition ◽  
Feature Extraction Method
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