scholarly journals Gender Classification in Human Face Images for Smart Phone Applications Based on Local Texture Information and Evaluated Kullback-Leibler Divergence

2019 ◽  
Vol 36 (6) ◽  
pp. 507-514 ◽  
Author(s):  
Shervan Fekri-Ershad
Keyword(s):  
Smart Phone ◽  
Gender Classification ◽  
Human Face ◽  
Local Texture ◽  
Face Images ◽  
Texture Information ◽  
Leibler Divergence

Extracting adaptive features for gender classification of human face images

2014 ◽  
Vol 40 (4) ◽  
pp. 215-221
Author(s):  
V. V. Khryashchev ◽  
L. A. Shmaglit ◽  
A. L. Priorov ◽  
A. M. Shemyakov
Keyword(s):  
Gender Classification ◽  
Human Face ◽  
Face Images

Private‐encoder: Enforcing privacy in latent space for human face images

2021 ◽  
Author(s):  
Yuan Zhao ◽  
Bo Liu ◽  
Tianqing Zhu ◽  
Ming Ding ◽  
Wanlei Zhou
Keyword(s):  
Human Face ◽  
Face Images ◽  
Latent Space

scholarly journals Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Front Face Images

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 385 ◽  
Author(s):  
Yoosoo Jeong ◽  
Seungmin Lee ◽  
Daejin Park ◽  
Kil Park
Keyword(s):  
Age Estimation ◽  
Metric Learning ◽  
Front Face ◽  
Experimental Fact ◽  
Gender Classification ◽  
Learning Method ◽  
Siamese Network ◽  
Task Learning ◽  
Face Images ◽  
Deep Metric Learning

Recently, there have been many studies on the automatic extraction of facial information using machine learning. Age estimation from front face images is becoming important, with various applications. Our proposed work is based on the binary classifier, which only determines whether two input images are clustered in a similar class, and trains the convolutional neural networks (CNNs) model using the deep metric learning method based on the Siamese network. To converge the results of the training Siamese network, two classes, for which age differences are below a certain level of distance, are considered as the same class, so the ratio of positive database images is increased. The deep metric learning method trains the CNN model to measure similarity based on only age data, but we found that the accumulated gender data can also be used to compare ages. From this experimental fact, we adopted a multi-task learning approach to consider the gender data for more accurate age estimation. In the experiment, we evaluated our approach using MORPH and MegaAge-Asian datasets, and compared gender classification accuracy only using age data from the training images. In addition, from the gender classification, we found that our proposed architecture, which is trained with only age data, performs age comparison by using the self-generated gender feature. The accuracy enhancement by multi-task learning, for the simultaneous consideration of age and gender data, is discussed. Our approach results in the best accuracy among the methods based on deep metric learning on MORPH dataset. Additionally, our method is also the best results compared with the results of the state of art in terms of age estimation on MegaAge Asian and MORPH datasets.

scholarly journals Detection of duplicate and non-face images in the eRecruitment applications using machine learning techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 114
Author(s):  
Manjunath K. E. ◽  
Yogeen S. Honnavar ◽  
Rakesh Pritmani ◽  
Sethuraman K.
Keyword(s):  
Machine Learning ◽  
Detection Algorithm ◽  
Similarity Score ◽  
Machine Learning Techniques ◽  
Human Face ◽  
Face Images ◽  
Research Organisation ◽  
Learning Techniques ◽  
Satellite Centre ◽  
Recruitment Activities

The objective of this work is to develop methodologies to detect, and report the noncompliant images with respect to indian space research organisation (ISRO) recruitment requirements. The recruitment software hosted at U. R. rao satellite centre (URSC) is responsible for handling recruitment activities of ISRO. Large number of online applications are received for each post advertised. In many cases, it is observed that the candidates are uploading either wrong or non-compliant images of the required documents. By non-compliant images, we mean images which do not have faces or there is not enough clarity in the faces present in the images uploaded. In this work, we attempt to address two specific problems namely: 1) To recognise image uploaded to recruitment portal contains a human face or not. This is addressed using a face detection algorithm. 2) To check whether images uploaded by two or more applications are same or not. This is achieved by using machine learning (ML) algorithms to generate similarity score between two images, and then identify the duplicate images. Screening of valid applications becomes very challenging as the verification of such images using a manual process is very time consuming and requires large human efforts. Hence, we propose novel ML techniques to determine duplicate and non-face images in the applications received by the recruitment portal.

scholarly journals RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization

2020 ◽  
Vol 34 (06) ◽  
pp. 10402-10409
Author(s):  
Tianying Wang ◽  
Wei Qi Toh ◽  
Hao Zhang ◽  
Xiuchao Sui ◽  
Shaohua Li ◽  
...  
Keyword(s):  
Face Image ◽  
Path Optimization ◽  
Robotic Arm ◽  
Human Face ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Face Images ◽  
Drawing System ◽  
Collaborative Robot

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the AvatarGAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.

Sign in / Sign up

Export Citation Format

Share Document