Robust Eye Gaze Estimation

Eye gaze detection under challenging lighting conditions is a non-trivial task. Pixel intensity and the shades around the eye region may change depending on the time of day, location, or due to artificial lighting. This paper introduces a lighting-adaptive solution for robust eye gaze detection. First, we propose a binarization and cropping technique to limit our region of interest. Then we develop a gradient-based method for eye-pupil detection; and finally, we introduce an adaptive eye-corner detection technique that altogether lead to robust eye gaze estimation. Experimental results show the outperformance of the proposed method compared with related techniques

Eye Status Based on Eyelid Detection: A Driver Assistance System

Fatigue and driver drowsiness monitoring is an important subject for designing driver assistance systems. The measurement of eye closure is a fundamental step for driver awareness detection. We propose a method which is based on eyelid detection and the measurement of the distance between the eyelids. First, the face and the eyes of the driver are localized. After extracting the eye region, the proposed algorithm detects eyelids and computes the percentage of eye closure. Experimental results are performed on the BioID database. Our comparisons show that the proposed method outperforms state-of-the-art methods

An Iterative and Toolchain-Based Approach to Automate Scanning and Mapping Computer Networks

As today's organizational computer networks are ever evolving and becoming more and more complex, finding potential vulnerabilities and conducting security audits has become a crucial element in securing these networks. The first step in auditing a network is reconnaissance by mapping it to get a comprehensive overview over its structure. The growing complexity, however, makes this task increasingly effortful, even more as mapping (instead of plain scanning), presently, still involves a lot of manual work. Therefore, the concept proposed in this paper automates the scanning and mapping of unknown and non-cooperative computer networks in order to find security weaknesses or verify access controls. It further helps to conduct audits by allowing comparing documented with actual networks and finding unauthorized network devices, as well as evaluating access control methods by conducting delta scans. It uses a novel approach of augmenting data from iteratively chained existing scanning tools with context, using genuine analytics modules to allow assessing a network's topology instead of just generating a list of scanned devices. It further contains a visualization model that provides a clear, lucid topology map and a special graph for comparative analysis. The goal is to provide maximum insight with a minimum of a priori knowledge.Comment: 7 pages, 6 figure

Indoor Outdoor Scene Classification in Digital Images

In this paper, we present a method to classify real-world digital images into indoor and outdoor scenes. Indoor class consists of four groups: bedroom, kitchen, laboratory and library. Outdoor class consists of four groups: landscape, roads, buildings and garden. Application considers real-time system and has a dedicated data-set. Input images are pre-processed and converted into gray-scale and is re-sized to “128x128” dimensions. Pre-processed images are sent to “Gabor filters”, which pre-computes filter transfer functions, which are performed on Fourier domain. The processed signal is finally sent to GIST feature extraction and the images are classified using “kNN classifier”. Most of the techniques have been based on the use of texture and color space features. As of date, we have been able to achieve 80% accuracy with respect to image classification

NUMERICAL APPLICATIONS OF THE METHOD OF HURWITZ-RADON MATRICES

Computer sciences need suitable methods for numerical calculations of interpolation, extrapolation, quadrature, derivative and solution of nonlinear equation. Classical methods, based on polynomial interpolation, have some negative features: they are useless to interpolate the function that fails to be differentiable at one point or differs from the shape of polynomial considerably, also the Runge‘s phenomenon cannot be forgotten. To deal with numerical interpolation, extrapolation, integration and differentiation dedicated methods should be constructed. One of them, called by author the method of Hurwitz-Radon Matrices (MHR), can be used in reconstruction and interpolation of curves in the plane. This novel method is based on a family of Hurwitz-Radon (HR) matrices. The matrices are skewsymmetric and possess columns composed of orthogonal vectors. The operator of Hurwitz- Radon (OHR), built from that matrices, is described. It is shown how to create the orthogonal and discrete OHR and how to use it in a process of function interpolation and numerical differentiation. Created from the family of N-1 HR matrices and completed with the identical matrix, system of matrices is orthogonal only for dimensions N = 2, 4 or 8. Orthogonality of columns and rows is very significant for stability and high precision of calculations. MHR method is interpolating the function point by point without using any formula of function. Main features of MHR method are: accuracy of curve reconstruction depending on number of nodes and method of choosing nodes, interpolation of L points of the curve is connected with the computational cost of rank O(L), MHR interpolation is not a linear interpolation

You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data

This work offers a design of a video surveillance system based on a soft biometric -- gait identification from MoCap data. The main focus is on two substantial issues of the video surveillance scenario: (1) the walkers do not cooperate in providing learning data to establish their identities and (2) the data are often noisy or incomplete. We show that only a few examples of human gait cycles are required to learn a projection of raw MoCap data onto a low-dimensional sub-space where the identities are well separable. Latent features learned by Maximum Margin Criterion (MMC) method discriminate better than any collection of geometric features. The MMC method is also highly robust to noisy data and works properly even with only a fraction of joints tracked. The overall workflow of the design is directly applicable for a day-to-day operation based on the available MoCap technology and algorithms for gait analysis. In the concept we introduce, a walker's identity is represented by a cluster of gait data collected at their incidents within the surveillance system: They are how they walk

Underwater Intention Recognition using Head Motion and Throat Vibration for Supernumerary Robotic Assistance

This study presents a multi-modal mechanism for recognizing human intentions while diving underwater, aiming to achieve natural human-robot interactions through an underwater superlimb for diving assistance. The underwater environment severely limits the divers' capabilities in intention expression, which becomes more challenging when they intend to operate tools while keeping control of body postures in 3D with the various diving suits and gears. The current literature is limited in underwater intention recognition, impeding the development of intelligent wearable systems for human-robot interactions underwater. Here, we present a novel solution to simultaneously detect head motion and throat vibrations under the water in a compact, wearable design. Experiment results show that using machine learning algorithms, we achieved high performance in integrating these two modalities to translate human intentions to robot control commands for an underwater superlimb system. This study's results paved the way for future development in underwater intention recognition and underwater human-robot interactions with supernumerary support.Comment: 6 pages, 9 figures, 3 tables, accepted to IEEE CASE 202

Eye status based on eyelid detection a driver assistance system

Fatigue and driver drowsiness monitoring is an important subject for designing driver assistance systems. The measurement of eye closure is a fundamental step for driver awareness detection. We propose a method which is based on eyelid detection and the measurement of the distance between the eyelids. First, the face and the eyes of the driver are localized. After extracting the eye region, the proposed algorithm detects eyelids and computes the percentage of eye closure. Experimental results are performed on the BioID database. Our comparisons show that the proposed method outperforms state-of-the-art methods. Document type: Part of book or chapter of boo