Age and Gender Classification of Tweets Using Convolutional Neural Networks

This paper describes the investigation results about the usage of shallow (limited by few layers only) convolutional neural networks (CNNs) to solve the video-based gender classification problem. Different architectures of shallow CNN are proposed, trained and tested using balanced and unbalanced static image datasets. The influence of diverse voting over confidences methods, applied for frame-by-frame gender classification of the video stream, is investigated for possible enhancement of the classification accuracy. The possibility of the grouping of shallow networks into ensembles is investigated; it has been shown that the accuracy may be more improved with the further voting of separate shallow CNN classification results inside an ensemble over a single frame or different ones.

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A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Sensors ◽

10.3390/s20020328 ◽

2020 ◽

Vol 20 (2) ◽

pp. 328 ◽

Cited By ~ 2

Author(s):

Khalil Khan ◽

Muhammad Attique ◽

Rehan Ullah Khan ◽

Ikram Syed ◽

Tae-Sun Chung

Keyword(s):

Neural Networks ◽

Image Analysis ◽

Convolutional Neural Networks ◽

Face Image ◽

Deep Convolutional Neural Networks ◽

Age And Gender ◽

Face Image Analysis ◽

Probability Maps ◽

End To End ◽

And Gender

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.

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

Journal of Sensors ◽

10.1155/2018/5034684 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

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.

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Molt Patterns, Biometrics, and Age and Gender Classification of Landbirds on Saipan, Northern Mariana Islands

The Wilson Journal of Ornithology ◽

10.1676/10-154.1 ◽

2011 ◽

Vol 123 (3) ◽

pp. 588-594 ◽

Cited By ~ 5

Author(s):

Paul Radley ◽

Andrea L. Crary ◽

James Bradley ◽

Christina Carter ◽

Peter Pyle

Keyword(s):

Gender Classification ◽

Mariana Islands ◽

Age And Gender ◽

Northern Mariana Islands ◽

And Gender

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