Gait recognition using kinect and locally linear embedding

This paper presents the use of locally linear embedding (LLE) as feature extraction technique for classifying a person’s identity based on their walking gait patterns. Skeleton data acquired from Microsoft Kinect camera were used as an input for (1). Multilayer Perceptron (MLP) and (2). LLE with MLP. The MLP classification accuracy result was used for comparison between both. Several MLP and LLE properties were tested to find the optimal number of setting that can improve the MLP performance. Based on the two methods used, the neural network implemented with LLE showed the better accuracy compared to the neural network alone.Keywords: locally linear embedding; neural network; multilayer perceptron

Human Gait Recognition from Motion Capture Data in Signature Poses

Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature -- there have been many good geometric features designed -- but to smartly process the data there are at our disposal. This work proposes a gait recognition method without design of novel gait features; instead, we suggest an effective and highly efficient way of processing known types of features. Our method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. We experimentally demonstrate that our gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment

One-Class Subject Identification From Smartphone-Acquired Walking Data

In this work, a novel type of human identification system is proposed, which has the aim to recognize a user from his biometric traits of his way of walk. A smartphone is utilized to acquire motion data from the built-in sensors. Data from accelerometer and gyroscope are processed through a cycle extraction phase, a Convolutional Neural Network for feature extraction and a One-Class SVM classifier for identification. From quantitave results the system achieves an Equal Error Rate close to 1

Hybrid Model for Passive Locomotion Control of a Biped Humanoid:The Artificial Neural Network Approach

Developing a correct model for a biped robot locomotion is extremely challenging due to its inherently unstable structure because of the passive joint located at the unilateral foot-ground contact and varying configurations throughout the gait cycle, resulting variation of dynamic descriptions and control laws from phase to phase. The present research describes the development of a hybrid biped model using an Open Dynamics Engine (ODE) based analytical three link leg model as a base model and, on top of it, an Artificial Neural Network based learning model which ensures better adaptability, better limits cycle behaviors and better generalization while negotiating along a down slope. The base model has been configured according to the individual subjects and data have been collected using a novel technique through an android app from those subjects while walking down a slope. The pattern between the deviation of the actual trajectories and the base model generated trajectories has been found using a back propagation based artificial neural network architecture. It has been observed that this base model with learning based compensation enables the biped to better adapt in a real walking environment, showing better limit cycle behaviors. We also observed the bounded nature of deviation which led us to conclude that the strategy for biped locomotion control is generic in nature and largely dominated by learning

Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication

Mobile devices have been manufactured and enhanced at growing rates in the past decades. While this growth has significantly evolved the capability of these devices, their security has been falling behind. This contrast in development between capability and security of mobile devices is a significant problem with the sensitive information of the public at risk. Continuing the previous work in this field, this study identifies key Machine Learning algorithms currently being used for behavioral biometric mobile authentication schemes and aims to provide a comprehensive review of these algorithms when used with touch dynamics and phone movement. Throughout this paper the benefits, limitations, and recommendations for future work will be discussed

An empirical biometric-based study for user identification with different neural networks in the online game League of Legends

The popularity of computer games has grown exponentially in the last years. Although such games were created to promote competition and promote self-improvement, there are some recurrent issues. One that has received the least amount of attention so far is the problem of 'account sharing' which is when a player shares his/her account with more experienced players to make progress in the game. The companies running those games tend to punish this behaviour, but this specific case is hard to identify. Since, the popularity of neural networks has never been higher, the aim of this study is to investigate how different neural network algorithms behave when analysing a database of biometric information (keystroke and mouse dynamics) regarding the game League of Legends, and how those algorithms are affected by how frequently a sample is collected

Analysis of Training Functions in a Biometric System

One of the commonly used Biometric methods is Face Classifica tion . Face images a re obtained from FEI face database. In this paper, different Training F unctio ns of Neural Network a re studied . In this research, a face recognition sy stem i s suggested based on feedforward backpro pagation neural network Neural Network (BPNN) model. Each model i s constructed separately with one input layer, 3 hidden layers and one output layer). Four ANN training algorithms (TRAINLM, TRAINBFG, TRAINGDX, and TRAINRP ) a re used to train each model se parately. Performances using each of the training algorithms were evaluated based on mean square error an d the best training algorithm i s found for the face recognition

Skeleton based gait recognition for long and baggy clothes

Human gait is a significant biometric feature used for the identification of people by their style of walking. Gait offers recognition from a distance at low resolution while requiring no user interaction. On the other hand, other biometrics are likely to require a certain level of interaction. In this paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thobe and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used to create five different datasets. Each dataset contained different combination of joints to explore their effectiveness. An evaluation experiment was carried out with 20 walking subjects, each having 25 walking sequences in total. The results achieved good recognition rates up to 97%