One of the most attractive biometric techniques is gait recognition, since its potential for human identification at a distance. But gait recognition is still challenging in real applications due to the effect of many variations on the appearance and shape. Usually, appearance-based methods need to compute gait energy image (GEI) which is extracted from the human silhouettes. GEI is an image that is obtained by averaging the silhouettes and as result the temporal information is removed. The body joints are invariant to changing clothing and carrying conditions. We propose a novel pose-based gait recognition approach that is more robust to the clothing and carrying variations. At the same time, a pose-based temporal-spatial network (PTSN) is proposed to extract the temporal-spatial features, which effectively improve the performance of gait recognition. Experiments evaluated on the challenging CASIA B dataset, show that our method achieves state-of-the-art performance in both carrying and clothing conditions.

Pose-Based Temporal-Spatial Network (PTSN) for Gait Recognition with Carrying and Clothing Variations

Improved gait recognition based on specialized deep convolutional neural network

Gait recognition is an emerging biometric technology that identifies people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations, carrying conditions and angle variations that adversely affect the recognition performance. This paper proposes a method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance. The proposed method is evaluated using CASIA Gait Dataset B. Experimental results demonstrate that the proposed technique gives promising results.

Human Body Part Selection by Group Lasso of Motion for Model-Free Gait Recognition

The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

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Deep Learning for Monitoring of Human Gait: A Review

Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-bmt.2018.5063

Robust gait recognition: a comprehensive survey

Biofilms are consortia of microorganisms encased in extracellular matrix that protect cells from adverse conditions. A biofilm matrix is typically composed of extracellular DNA, cellulose and proteinaceous amyloid fibers. The matrix aids in adhesion to abiotic and biotic surface including medical devices and host tissues. The presence of biofilm makes bacteria more resilient and non-responsive to most current treatment regimes at disposal. Therefore, biofilm-associated infections are serious threat in hospital settings and pose a huge burden on economy. Inhibition of matrix components (cellulose and/or amyloid formation) has emerged as a lucrative alternative strategy to cure biofilm-related infections and combat antibiotic resistance. Here we review the current and emerging therapeutic interventions to mitigate persistent infections due to biofilms. The successful implementation of these interventions will have a huge impact on alleviating the current financial burden on healthcare services.

Neha Jain

Papers

Stopping the unstoppable: Unconventional methods to prevent the biofilm growth