Age and Gender Recognition from Speech Using Deep Neural Networks

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.

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Deep Neural Networks for Recommender Systems

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3706.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 4838-4842 ◽

Cited By ~ 1

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.

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Understanding and Comparing Deep Neural Networks for Age and Gender Classification

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) ◽

10.1109/iccvw.2017.191 ◽

2017 ◽

Cited By ~ 7

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

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Age and Gender Prediction using Face Recognition

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3275.1211221 ◽

2021 ◽

Vol 11 (2) ◽

pp. 48-51

Author(s):

Sai Teja Challa ◽

◽

Sowjanya Jindam ◽

Ruchitha Reddy Reddy ◽

Kalathila Uthej ◽

...

Keyword(s):

Neural Networks ◽

State Of The Art ◽

Gender Recognition ◽

Deep Convolutional Neural Networks ◽

Age And Gender ◽

Face Images ◽

Wide Range ◽

Frame Work ◽

And Gender ◽

Learning Data

Automatic age and gender prediction from face images has lately attracted much attention due to its wide range of applications in numerous facial analyses. We show in this study that utilizing the Caffe Model Architecture of Deep Learning Frame Work; we were able to greatly enhance age and gender recognition by learning representations using deep-convolutional neural networks (CNN). We propose a much simpler convolutional net architecture that can be employed even if no learning data is available. In a recent study presenting a potential benchmark for age and gender estimation, we show that our strategy greatly outperforms existing state-of-the-art methods.

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