A Study on Gait-Based Gender Classification

By Shiqi Yu, Tien-Ping Tan, Kaiqi Huang, K. Jia, Xinyu Wu. IEEE Transactions on Image Processing, 2009

306 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/35058a8166a8fa4479167ba33b3010cc8c839f44

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Pitfalls in Machine Learning Research: Reexamining the Development Cycle (2020, unknown): “There are hundreds of papers that claim to be able to accurately determine people’s gender based on anything from a writing sample [6, 56, 20], to their gait [57, 16, 18], to the texture of their skin [54, 27, 1].”
Pattern Recognition (2018, unknown): “[21] incorporated human knowledge to design their model while the proposed method does not rely on any prior knowledge.”
iLGaCo: Incremental Learning of Gait Covariate Factors (2020, background): “Because of these advantages, research on gait recognition is becoming more and more popular and has also made great progress in recent years [25, 37, 38, 43, 46].”
Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition (2012, methodology): “We can find in the literature several approaches for gait classification [3,5,15,6,9].”
The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition (2012, methodology): “Moreover, if the database is used for soft biometric applications such as gait-based gender and age classification [10], [11], the diversity of subjects in terms of gender and age plays a significant role in the performance evaluation.”
Gender Recognition or Gender Reductionism?: The Social Implications of Embedded Gender Recognition Systems (2018, methodology): “Researchers have focused on several methods for implementing AGR and improving its accuracy, including: voice recognition using fundamental frequency and MFCC coefficients [46], facial recognition using image texture extraction [27] and face alignment [30], body recognition using a part-based gender”