scholarly journals Improving Gender Classification Accuracy in the Wild

2013 ◽  
pp. 270-277 ◽  
Author(s):  
Modesto Castrillón-Santana ◽  
Javier Lorenzo-Navarro ◽  
Enrique Ramón-Balmaseda
Keyword(s):  
Classification Accuracy ◽  
Gender Classification ◽  
In The Wild

scholarly journals Gender classification using custom convolutional neural networks architecture

2020 ◽  
Vol 10 (6) ◽  
pp. 5758
Author(s):  
Fadhlan Hafizhelmi Kamaru Zaman
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
High Accuracy ◽  
Input Image ◽  
Gender Classification ◽  
Specific Task ◽  
Male And Female ◽  
Computation Efficiency ◽  
The Arts ◽  
Facial Images

Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed Convolutional Neural Network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some popular state-of-the-arts such as GoogleNet and AlexNet. It also employs batch normalization layers which results in better computation efficiency. Based on experiments using publicly available datasets such as LFW, CelebA and IMDB-WIKI datasets, the proposed custom CNN delivered the fastest inference time in all tests, where it needs only 0.92ms to classify 1200 images on GPU, 1.79ms on CPU, and 2.51ms on VPU. The custom CNN also delivers performance on-par with state-of-the-arts and even surpassed these methods in CelebA gender classification where it delivered the best result at 96% accuracy. Moreover, in a more challenging cross-dataset inference, custom CNN trained using CelebA dataset gives the best gender classification accuracy for tests on IMDB and WIKI datasets at 97% and 96% accuracy respectively.

Coarse and Fine: A New Method for Gender Classification in the Wild

2017 ◽  
pp. 162-171
Author(s):  
Qianbo Jiang ◽  
Li Shao ◽  
Zhengxi Liu ◽  
Qijun Zhao
Keyword(s):  
New Method ◽  
Gender Classification ◽  
In The Wild

Video Stream Gender Classification Using Shallow CNN

2020 ◽  
pp. 2155001
Author(s):  
Oleksii Gorokhovatskyi ◽  
Olena Peredrii
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Classification Problem ◽  
Video Stream ◽  
Gender Classification ◽  
Single Frame ◽  
Static Image ◽  
Image Datasets

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.

scholarly journals Multi-scale score level fusion of local descriptors for gender classification in the wild

2016 ◽  
Vol 76 (4) ◽  
pp. 4695-4711 ◽  
Author(s):  
M. Castrillón-Santana ◽  
J. Lorenzo-Navarro ◽  
E. Ramón-Balmaseda
Keyword(s):  
Scale Score ◽  
Gender Classification ◽  
Score Level Fusion ◽  
Local Descriptors ◽  
Multi Scale ◽  
In The Wild ◽  
Level Fusion

scholarly journals Descriptors and regions of interest fusion for in- and cross-database gender classification in the wild

2017 ◽  
Vol 57 ◽  
pp. 15-24 ◽  
Author(s):  
M. Castrillón-Santana ◽  
J. Lorenzo-Navarro ◽  
E. Ramón-Balmaseda
Keyword(s):  
Regions Of Interest ◽  
Gender Classification ◽  
In The Wild

scholarly journals Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks

Inventions ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 16
Author(s):  
Md. Mahbubul Islam ◽  
Nusrat Tasnim ◽  
Joong-Hwan Baek
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Pareto Frontier ◽  
Gender Classification ◽  
Learning Networks ◽  
Machine Learning Methods ◽  
Image Dataset ◽  
Internet Images ◽  
Learning Concept

Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

On using periocular biometric for gender classification in the wild

2016 ◽  
Vol 82 ◽  
pp. 181-189 ◽  
Author(s):  
Modesto Castrillón-Santana ◽  
Javier Lorenzo-Navarro ◽  
Enrique Ramón-Balmaseda
Keyword(s):  
Gender Classification ◽  
In The Wild
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