Data Mining Practical Machine Learning Tools and Techniques

Book review: Applied cryptography: Protocols, algorithms, and source code in C

In the digital world of today, global security issues have given rise to video surveillance devices. Gait-based human recognition is an emerging behavioral biometric trait for intelligent surveillance monitoring because of its non-contact and non-cooperation with subjects. Other benefits of gait recognition in video surveillance are that it can be acquired at a distance and help to identify an object under low-resolution videos. This paper surveys extensively the current progress made towards vision-based human gait recognition. This paper discusses historical research that performs analysis of gait locomotion and provides information on how gait recognition can be performed. This paper describes measuring metrics that can be used to measure the performance of gait recognition model under verification and identification mode. This paper also provides an up-to-date review of existing studies on gait recognition representations (model based and model free). We also provide an extensive survey of available gait databases used in state-of-art gait recognition models, created since 1998. Furthermore, it offers insight into open research problems that help researchers to explore unripe areas in gait analysis, such as occlusion, view variations, and appearance changes in gait recognition. This paper also identifies the future perspectives in gait recognition and also outlines the proposed work.

Vision-Based Gait Recognition: A Survey

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

— While Handwritten Signature Verification (HSV) techniques thwart attacks on the security associated with authentication, the signature itself may be prone to attacks with respect to data security. Hence it is vital to store the signatures as encrypted parameters. But storage of the signature as parameters of features would mean that it would not be possible to retrieve the original signature image for cross referencing in case of any error such as a false rejection. Though image encryption may provide end-to-end security, it requires the image to be reproduced at least temporarily on to the memory of the system on which the signature verification is to be performed. This leads to the vulnerability of what is called an image shadowing attack also known as a dead-forensic attack. This article poses a technique that allows the HSV system to store the original reference signatures as a set of encrypted curve equations providing added confidentiality to the signatures as well as facilitating the signatures to be retrievable in case of any discrepancies in the signature verification algorithm.

/pdf/handwritten-signature-confidentiality-through-segmented-2fu321sp5z.pdf

Handwritten Signature Confidentiality through Segmented Curve Generation

Gait analysis is the study of the systematic methods that assess and quantify animal locomotion. Gait finds a unique importance among the many state-of-the-art biometric systems since it does not require the subject's cooperation to the extent required by other modalities. Hence by nature, it is an unobtrusive biometric. This thesis discusses both hard and soft biometric characteristics of gait. It shows how to identify gender based on gait alone through the Posed-Based Voting scheme. It then describes improving gait recognition accuracy using Genetic Template Segmentation. Members of a wide population can be authenticated using Multiperson Signature Mapping. Finally, the mapping can be improved in a smaller population using Bayesian Thresholding. All methods proposed in this thesis have outperformed their existing state of the art with adequate experimentation and results.

Ebenezer R. H. P. Isaac

Papers

Handwritten Signature Confidentiality through Segmented Curve Generation

Robust Analytics for Video-Based Gait Biometrics.