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

Template-based model-free approach provides by far the most successful solution to the gait recognition problem in literature. Recent work discusses how isolating the head and leg portion of the template increase the performance of a gait recognition system making it robust against covariates like clothing and carrying conditions. However, most involve a manual definition of the boundaries. The method we propose, the genetic template segmentation, employs the genetic algorithm to automate the boundary selection process. This method was tested on the gait energy image (GEI), gait entropy image, and active energy image templates. GEI seems to exhibit the best result when segmented with our approach. Experimental results depict that our approach significantly outperforms the existing implementations of view-invariant gait recognition.

View-Invariant Gait Recognition Through Genetic Template Segmentation

Multiview gait-based gender classification through pose-based voting

With an ever growing need to classify multispectral images, the accuracy of the classification becomes a matter of concern, especially when mapping heterogeneous environments such as urban areas. N...

Urban landcover classification from multispectral image data using optimized AdaBoosted random forests

Gait analysis is the study of the systematic methods that assess and quantify animal locomotion. The research on gait analysis has considerably evolved through time. It was an ancient art, and it still finds its application today in modern science and medicine. This paper describes how one's gait can be used as a biometric. It shall diversely cover salient research done within the field and explain the nuances and advances in each type of gait analysis. The prominent methods of gait recognition from the early era to the state of the art are covered. This survey also reviews the various gait datasets. The overall aim of this study is to provide a concise roadmap for anyone who wishes to do research in the field of gait biometrics.

Trait of Gait: A Survey on Gait Biometrics

Many data encryption techniques have been employed to ensure both personal data security and network security. But few have been successful in merging both under one roof. The block cipher techniques commonly used for personal security such as DES and AES run multiple passes over each block making them ineffective for real time data transfer. Also, ciphers for network security such as Diffie-Hellman and RSA require large number of bits. This paper suggests a simple block cipher scheme to effectively reduce both time and space complexities and still provide adequate security for both security domains. The proposed Reverse Circle Cipher uses ‘circular substitution’ and ‘reversal transposition’ to exploit the benefits of both confusion and diffusion. This scheme uses an arbitrarily variable key length which may even be equal to the length of the plaintext or as small as a few bits coupled with an arbitrary reversal factor. This method of encryption can be utilized within stand alone systems for personal data security or even streamed into real time packet transfer for network security. This paper also analyses the effectiveness of the algorithm with respect to the size of the plaintext and frequency distribution within the ciphertext.

Ebenezer R. H. P. Isaac

Papers

View-Invariant Gait Recognition Through Genetic Template Segmentation

Multiview gait-based gender classification through pose-based voting

Urban landcover classification from multispectral image data using optimized AdaBoosted random forests

Trait of Gait: A Survey on Gait Biometrics

Reverse Circle Cipher for personal and network security