Investigations into the Robustness of Audio-Visual Gender Classification to Background Noise and Illumination Effects

Stewart, Darryl; Wang, Hongbin; Shen, Jiali; Miller, Paul

doi:10.1109/dicta.2009.34

Cited by 4 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This number, 86%, is very similar to that reported by the authors in their paper 83% (Stewart et al, 2009). Although the performance of this classifier is reasonably high, it must be noticed that, since it is the only sensor evaluating gender, an error in the gender recognition cannot be corrected by further reasoning.…”

Section: Video Analytics Performacesupporting

confidence: 73%

See 1 more Smart Citation

Hierarchical Task Network planning with common-sense reasoning for multiple-people behaviour analysis

Santofimia

Rincón

Hong

et al. 2017

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

Safety on public transport is a major concern for the relevant authorities. We address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities.Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.

show abstract

Section: Video Analytics Performacesupporting

confidence: 73%

“…Both PCA and SVM system is a label with the gender of the passenger as well as a confidence value or probability of the face as being either male or female. The accuracy of this video analytic module was reported to be 83% in an independent testing (Stewart et al, 2009). …”

Section: The Computer Vision Systemmentioning

confidence: 99%

Hierarchical Task Network planning with common-sense reasoning for multiple-people behaviour analysis

Santofimia

Rincón

Hong

et al. 2017

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…With these applications in mind and with improvements in computational ability and classification algorithms there has been a good deal of recent interest in visual-based automatic gender classification systems. Much of the work has been concerned with full faces [1][2][3][4], although promising results have also been obtained using alternative representations such as gait [5] or full body images [6] and even hands [7]. It is understood, however, that individuals under surveillance by CCTV are not always cooperative (knowingly or unknowingly) and it may be difficult to capture certain traits cleanly in video footage.…”

Section: Introductionmentioning

confidence: 99%

Gender classification via lips: static and dynamic features

2013

Self Cite

View full text Add to dashboard Cite

Automatic gender classification has many security and commercial applications. Various modalities have been investigated for gender classification with face-based classification being the most popular.In some real-world scenarios the face may be partially occluded. In these circumstances a classification based on individual parts of the face known as local features must be adopted. We investigate gender classification using lip movements. We show for the first time that important gender specific information can be obtained from the way in which a person moves their lips during speech. Furthermore our study indicates that the lip dynamics during speech provide greater gender discriminative information than simply lip appearance. We also show that the lip dynamics and appearance contain complementary gender information such that a model which captures both traits gives the highest overall classification result. We use Discrete Cosine Transform based features and Gaussian Mixture Modelling to model lip appearance and dynamics and employ the XM2VTS database for our experiments. Our experiments show that a model which captures lip dynamics along with appearance can improve gender classification rates by between 16-21% compared to models of only lip appearance.

show abstract

Noise-Robust Gender Classification System Through Optimal Selection of Acoustic Features

Bawa

Kumar

Kadyan

et al. 2021

Signals and Communication Technology

View full text Add to dashboard Cite

Investigations into the Robustness of Audio-Visual Gender Classification to Background Noise and Illumination Effects

Cited by 4 publications

References 22 publications

Hierarchical Task Network planning with common-sense reasoning for multiple-people behaviour analysis

Hierarchical Task Network planning with common-sense reasoning for multiple-people behaviour analysis

Gender classification via lips: static and dynamic features

Noise-Robust Gender Classification System Through Optimal Selection of Acoustic Features

Contact Info

Product

Resources

About