Derlin Morocho

Towards human-assisted signature recognition: Improving biometric systems through attribute-based recognition

This work explores human-assisted schemes for improving automatic signature recognition systems. We present a crowdsourcing experiment to establish the human baseline performance for signature recognition tasks and a novel attribute-based semi-automatic signature verification system inspired in FDE analysis. We present different experiments over a public database and a self-developed tool for the manual annotation of signature attributes. The results demonstrate the benefits of attribute-based recognition approaches and encourage to further research in the capabilities of human intervention to improve the performance of automatic signature recognition systems.

Signature recognition: establishing human baseline performance via crowdsourcing

This work explores crowdsourcing for the establishment of human baseline performance on signature recognition. We present five experiments according to three different scenarios in which laymen, people without Forensic Document Examiner experience, have to decide about the authenticity of a given signature. The scenarios include single comparisons between one genuine sample and one unlabeled sample based on image, video or time sequences and comparisons with multiple training and test sets. The human performance obtained varies from 7% to 80% depending of the scenario and the results suggest the large potential of these collaborative platforms and encourage to further research on this area.

On the evaluation of human ratings for signature recognition

This work explores the human ability to recognize the authenticity of signatures. We use crowdsourcing to analyze the different factors affecting the performance of humans without Forensic Document Examiner experience. We present different experiments according to different scenarios in which laymen, people without Forensic Document Examiner experience, provide similarity measures related with the perceived authenticity of a given signature. The human responses are used to analyze the performance of humans according to each of the scenarios and main factors. The experiments comprise 240 signatures from BiosecurlD public database and responses from more than 400 people. The results shows the difficulties associated to these tasks, with special attention to the false acceptance of forgeries with rates ranging from 50% to 75%. The results suggest that human recognition abilities in this scenario are strongly dependent on the characteristics considered and the signature at hand. Finally the combination of human ratings clearly outperfoms the individual performance and and a state-of-the-art automatic signature verification system.

Signature authentication based on human intervention: performance and complementarity with automatic systems

This work explores human intervention to improve Automatic Signature Verification (ASV). Significant efforts have been made in order to improve the performance of ASV algorithms over the last decades. This work analyzes how human actions can be used to complement automatic systems. Which actions to take and to what extent those actions can help state-of-the-art ASV systems is the final aim of this research line. The analysis at classification level comprises experiments with responses from 500 people based on crowdsourcing signature authentication tasks. The results allow to establish a human baseline performance and comparison with automatic systems. Intervention at feature extraction level is evaluated using a self-developed tool for the manual annotation of signature attributes inspired in Forensic Document Experts analysis. We analyze the performance of attribute-based human signature authentication and its complementarity with automatic systems. The experiments are carried out over a public database including the two most popular signature authentication scenarios based on both online (dynamic time sequences including position and pressure) and offline (static images) information. The results demonstrate the potential of human interventions at feature extraction level (by manually annotating signature attributes) and encourage to further research in its capabilities to improve the performance of ASV.

Development and implementation of an application that translates the alphabet and the numbers from 1 to 10 from sign language to text to help hearing impaired by Android mobile devices

This article is based on the design and implementation of an application for mobile devices with Android operating system, which allows the interrelation of people with hearing impairment. The application is able to learn and recognize a letter or number sign language with no movement by applying artificial neural networks. The application indicates whether the captured image is part of the letter to be recognized, if the image does not belong to the corresponding letter, the application displays an error message.

Analysis of attack and protection systems in Wi-Fi wireless networks under the Linux operating system

This article describes the preparation of a Wi-Fi wireless network in production system, with intrusion detection systems Snort and Kismet; for subsequent evaluation under attack. Through Penetration Testing with Backtrack 5 R3 using Fern WiFi Cracker and Ettercap to monitor response reaction of IDSs. Once the attacks are completed, the results are analyzed, in terms of the captured traffic by the system using Wireshark, and the attack description, in order to determine the response characteristics of Snort and Kismet. In addition, minimum safety recommendations are deducted, targeting both network administrators and clients; to avoid problems with the attackers.

Design and construction of an anthropomorphic robotic arm of seven degrees of freedom with kinematic and dynamic analysis based on genetic algorithms

This article describes the design and construction of an anthropomorphic robotic arm capable of performing activities difficult or avoid collisions within your workspace, which is controlled by a computer program where the user indicates the target position and orientation to be achieved by the robot TCP. The software programmed in Matlab is developed, it calculates the angular displacements of the actuators using Genetic Algorithms (GA) considered as restricting the minimum and maximum angular displacement of each motor. The program has a graphical interface that displays a 3D robot animation, displays graphs of torque, velocity and acceleration at each joint also allows you to generate trajectories based on programmed points. The robot is connected to the computer using an USB connection and has an USB2Dynamixel device through which communication with computer controllers and servomotors Dynamixel is managed.

Design and implementation of a control of position system for the quadricopter AR. Drone 2.0 by Parrot

This article describes experimental develop of control position without considerate trajectory that realize the quadricopter, realizing the essays in different highs about above sea level. Using parametric identification to obtain the model, its designed a PID control with positions “x” and “y” by using Routh Hurwitz method to determinate the parameters. Finally it simulates the control in Matlab which presents the test and results of real system.

State of the art: Humans performance contributions in signature recognition via crowdsourcing and manual annotation

This work visualizes a state - of - the - art study of researches in the biometry specially in signature recognition, to show the potential of collaborative tools such as crowdsourcing and a tool for human - assisted schemes to improve automatic signature recognition systems. We present an analysis of experiments of evaluation of signatures made through crowdsourcing and labeling of attributes inspired by the FDE analysis. These experiments allow us to establish human performance in signature recognition tasks, the same ones that they use, an HTML interface through the Mturk platform, a public BiosecureID database, and an interface developed in Matlab for manual annotation. The results of these studies demonstrate how human intervention can help improve the performance of automatic signature recognition systems and be able to propose a semi-automatic system schema.

Humans in the loop: Study of semi-automatic signature recognition based on attributes

The present work analyzes performance, abilities and contributions of the human being (layman) in semi-automatic signature recognition systems. During the last decade the performance of Automatic Signature Verification systems have been improved based on new machine learning techniques and better knowledge about intraclass and interclass variability of signers. However, there is still room for improvements and some real world applications demands lower error rates. This work analyzes collaborative tools such as crowdsourcing and human-assisted schemes developed to improve Automatic Signature Verification systems. The performance of humans in semi-automatic recognition tasks is directly related to the information provided during the comparisons. How humans can help automatic systems goes from direct forgery detection to semiautomatic attribute labeling. In this work, we present recent advances, analyzing their performance according to the same experimental protocol. The results suggest the potential of comparative attributes as a way to improve Automatic Signature Verification systems.