Automatic Recognition of Speaker Age and Gender Based on Deep Neural Networks

2019 ◽  
pp. 327-336 ◽  
Author(s):  
Maxim Markitantov ◽  
Oxana Verkholyak
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Recognition ◽  
Age And Gender ◽  
Gender Based ◽  
And Gender

Age and Gender Recognition from Speech Using Deep Neural Networks

2020 ◽  
pp. 332-344
Author(s):  
Héctor A. Sánchez-Hevia ◽  
Roberto Gil-Pita ◽  
Manuel Utrilla-Manso ◽  
Manuel Rosa-Zurera
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Gender Recognition ◽  
Age And Gender ◽  
And Gender

Age and gender classification from speech and face images by jointly fine-tuned deep neural networks

2017 ◽  
Vol 85 ◽  
pp. 76-86 ◽  
Author(s):  
Zakariya Qawaqneh ◽  
Arafat Abu Mallouh ◽  
Buket D. Barkana
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Gender Classification ◽  
Age And Gender ◽  
Face Images ◽  
And Gender

scholarly journals Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yungang Zhang ◽  
Tianwei Xu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Global Network ◽  
Gender Classification ◽  
Local Networks ◽  
Age And Gender ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Model Training ◽  
And Gender

Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a “global” network. The age and gender classification results are obtained by combining both the outputs from both the “global” and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.

scholarly journals Deep Neural Networks for Recommender Systems

2019 ◽  
Vol 8 (12) ◽  
pp. 4838-4842 ◽  
Keyword(s):  
Neural Networks ◽  
Recommender Systems ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Recommendation Systems ◽  
Online Reading ◽  
Age And Gender ◽  
Area Of Interest ◽  
Feed Forward Neural Networks ◽  
And Gender

The data available online, helps users to get information about anything of his/her interest. But since the data is huge and complex it is difficult to get useful information from it. Recommender Systems are effective software techniques to overcome this problem. Based on the user’s and item’s information available, these techniques provide recommendations to users in their area of interest. Recommender systems have wide applications like providing suggestive list of items to customers for online shopping, recommending articles or books for online reading, movie or music recommendations, news recommendations etc. In this paper we provide a study of Deep Neural Networks (DNN) approaches that can be used for recommender systems. They have been used widely in last decade in many fields like image processing, video streaming, Natural Language Processing etc. including recommendations to overcome the drawbacks of traditional systems. The paper also provides performance of Denoising AutoEncoders (DAE) which are feed forward neural networks and its comparison with traditional systems. Denoising Autoencoders are a type of autoencoders wherein some part of input is corrupted, i.e., noise is added to the input. While learning to remove noise from input, the DAE also learns to predict unknown values. This property of Denoising Autoencoders can help in recommendation systems to predict unknown values before recommending new items. Experimentation has shown improvement in the performance of recommendation systems with denoising autoencoders. The evaluation is performed on MovieLens-1M dataset with and without additional features of users (age and gender) and items (movie genres) provided in the dataset.

scholarly journals Understanding and Comparing Deep Neural Networks for Age and Gender Classification

2017 ◽  
Author(s):  
Wojciech Samek ◽  
Alexander Binder ◽  
Sebastian Lapuschkin ◽  
Klaus-Robert Muller
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Gender Classification ◽  
Age And Gender ◽  
And Gender

Age- and Gender-Based Patterns in Youth All-Terrain Vehicle (ATV) Riding Behaviors

2016 ◽  
Vol 21 (2) ◽  
pp. 163-170 ◽  
Author(s):  
Hamida Jinnah ◽  
Zolinda Stoneman
Keyword(s):  
Age And Gender ◽  
Gender Based ◽  
And Gender

Age and gender-based human face reconstruction from single frontal image

2018 ◽  
Vol 79 (5-6) ◽  
pp. 3217-3242 ◽  
Author(s):  
Zuzana Ferková ◽  
Petra Urbanová ◽  
Dominik Černý ◽  
Marek Žuži ◽  
Petr Matula
Keyword(s):  
Human Face ◽  
Age And Gender ◽  
Face Reconstruction ◽  
Gender Based ◽  
And Gender

188 The complex relationship between preoperative hemoglobin and the risks of cardiac surgery: An age- and gender-based analysis

2011 ◽  
Vol 27 (5) ◽  
pp. S128
Author(s):  
L. Zhang ◽  
B. Hiebert ◽  
R. Zarychanski ◽  
R. Arora
Keyword(s):  
Cardiac Surgery ◽  
Complex Relationship ◽  
Age And Gender ◽  
Preoperative Hemoglobin ◽  
Gender Based ◽  
And Gender

scholarly journals Escherichia coli as uropathogen: antibiotic susceptibility profiling

2018 ◽  
Vol 7 (3) ◽  
pp. 433
Author(s):  
Anuja Jha ◽  
Manju Agrawal ◽  
Arvind Neral ◽  
Rajesh Hishikar ◽  
Basant Maheshwari
Keyword(s):  
Empirical Therapy ◽  
Resistance Pattern ◽  
P Value ◽  
Age And Gender ◽  
Exact Test ◽  
Significant Difference ◽  
Gender Based ◽  
And Gender ◽  
Tract Infections ◽  
Uncomplicated Cystitis

Background: Empirically chosen antibiotics based on the local resistance pattern of uropathogens remain the principle treatment of urinary tract infections (UTI).Methods: Antibiogram of most frequent uropathogen was determined. Based on the antibiogram result, authors compared effectiveness of drugs recommended for UTI by National centre for disease control (NCDC), India, and assessed age and gender based variability in the effectiveness of these drugs.Results: 1278 urine samples were accounted, of which 405 samples showed significant growth. E. coli was the most common uropathogen (n=146, 36%) followed by enterococcus species (31%) and Klebsiella pneumoniae (10%). Using McNemar’s test authors found that nitrofurantoin (90% sensitivity) was statistically the most effective drug among drugs recommended by NCDC for uncomplicated cystitis. Furthermore, authors used Fisher’s exact test on adults and paediatrics and found that significant difference in effectiveness was observed for nitrofurantoin (p-value <0.001) and cotrimoxazole (p-value 0.034). Using logistic regression, authors found that with age, effectiveness of ciprofloxacin and cotrimoxazole deteriorate significantly (p-value 0.021 and 0.002 respectively). Additionally, authors observed that cotrimoxazole has significantly better efficacy in males compared to females (p-value 0.022).Conclusions: In accordance with present study, nitrofurantoin can be used as first line treatment for uncomplicated cystitis. Age and gender should be considered while prescribing empirical treatment for UTI. Periodic surveillance should be carried out to identify the on-going pattern of antibiogram to update the guideline for empirical therapy.

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