Javier Guerra-Casanova

Analysis of pattern recognition techniques for in-air signature biometrics

As a result of advances in mobile technology, new services which benefit from the ubiquity of these devices are appearing. Some of these services require the identification of the subject since they may access private user information. In this paper, we propose to identify each user by drawing his/her handwritten signature in the air (in-air signature). In order to assess the feasibility of an in-air signature as a biometric feature, we have analysed the performance of several well-known pattern recognition techniques-Hidden Markov Models, Bayes classifiers and dynamic time warping-to cope with this problem. Each technique has been tested in the identification of the signatures of 96 individuals. Furthermore, the robustness of each method against spoofing attacks has also been analysed using six impostors who attempted to emulate every signature. The best results in both experiments have been reached by using a technique based on dynamic time warping which carries out the recognition by calculating distances to an average template extracted from several training instances. Finally, a permanence analysis has been carried out in order to assess the stability of in-air signature over time.

Authentication in mobile devices through hand gesture recognition

This article proposes an innovative biometric technique based on the idea of authenticating a person on a mobile device by gesture recognition. To accomplish this aim, a user is prompted to be recognized by a gesture he/she performs moving his/her hand while holding a mobile device with an accelerometer embedded. As users are not able to repeat a gesture exactly in the air, an algorithm based on sequence alignment is developed to correct slight differences between repetitions of the same gesture. The robustness of this biometric technique has been studied within 2 different tests analyzing a database of 100 users with real falsifications. Equal Error Rates of 2.01 and 4.82% have been obtained in a zero-effort and an active impostor attack, respectively. A permanence evaluation is also presented from the analysis of the repetition of the gestures of 25 users in 10 sessions over a month. Furthermore, two different gesture databases have been developed: one made up of 100 genuine identifying 3-D hand gestures and 3 impostors trying to falsify each of them and another with 25 volunteers repeating their identifying 3-D hand gesture in 10 sessions over a month. These databases are the most extensive in published studies, to the best of our knowledge.

Unconstrained and Contactless Hand Geometry Biometrics

This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison of the performance to two competitive pattern recognition techniques existing in literature: namely Support Vector Machines (SVM) and k-Nearest Neighbour (k-NN). Results highlight the fact that the proposed method outcomes existing approaches in literature in terms of computational cost, accuracy in human identification, number of extracted features and number of samples for template creation. The proposed method is a suitable solution for human identification in contact-less scenarios based on hand biometrics, providing a feasible solution to devices with limited hardware requirements like mobile devices.

Supervised classification methods applied to Keystroke Dynamics through Mobile Devices

Keystroke dynamics biometrics through computers are based in the time that users need to press and hold keys and often present too small amount of information. This limitation is eliminated in the environment of mobile devices due to a variety of sensors (accelerometers, gyroscopes, pressure and finger size) can be used to acquire useful information from users. These data have been acquired within the scenario of typing a 4-digit PIN in order to analyze the possibilites of reinforcing the security of mobile devices. A database with keystroke dynamics patterns has been analysed. Data has been acquired in a constrained environment, where users must hold the phone in a fixed position, and other with the data taken in unconstrained conditions. Features as pressure, finger size, times, linear an angular acceleration are extracted and processed. Supervised classification methods are widely used in different kind of biometrics. A discussion about their use in keystroke biometrics is presented. A preprocessing of the acquired data is performed using Linear Discriminant Analysis (LDA) and a reduction of the amount of information applying Principal Components Analysis (PCA). This preprocessing enhances considerably the results obtained in classification. We conclude claiming that biometric systems through keystroke dynamics with 4-digit PIN are promising.

Time series distances measures to analyze in-air signatures to authenticate users on mobile phones

Improving the security of mobile phones is one of the crucial points required to assure the personal information and the operations that can be performed from them. This article presents an authentication procedure consisting of verifying the identity of people by making a signature in the air while holding the mobile phone. Different temporal distance algorithms have been proposed and evaluated through a database of 50 people making their signatures in the air and 6 people trying to forge each of them by studying their records. Approaches based on DTW have obtained better EER results than those based on LCS (2.80% against 3.34%). Besides, different signal normalization methods have been evaluated not finding any with better EER results that when no normalization has carried out.

Score optimization and template updating in a biometric technique for authentication in mobiles based on gestures

This article focuses on the evaluation of a biometric technique based on the performance of an identifying gesture by holding a telephone with an embedded accelerometer in his/her hand. The acceleration signals obtained when users perform gestures are analyzed following a mathematical method based on global sequence alignment. In this article, eight different scores are proposed and evaluated in order to quantify the differences between gestures, obtaining an optimal EER result of 3.42% when analyzing a random set of 40 users of a database made up of 80 users with real attempts of falsification. Moreover, a temporal study of the technique is presented leeding to the need to update the template to adapt the manner in which users modify how they perform their identifying gesture over time. Six updating schemes have been assessed within a database of 22 users repeating their identifying gesture in 20 sessions over 4 months, concluding that the more often the template is updated the better and more stable performance the technique presents.

gb2sμMOD: A MUltiMODal biometric video database using visible and IR light

Abstract In spite of recent efforts in gathering multimodal databases containing a big number of traits, a huge amount of users and covering multiple realistic scenarios, there is still a lack of touch-less realistic samples, video recordings for some traits and the use of infrared cameras which allows, among others, to avoid lighting influence and test recently appeared biometric techniques such as hand vein recognition. For this reason, a new realistic multimodal database composed of 8,160 hand, iris and face videos has been captured. To this end, a total of 60 contributors have participated in three separated acquisition sessions in which three different cameras have been used, covering different ranges of the light spectrum: visible light and two different infrared wavelengths. To simulate real-world working conditions, the database has been recorded in an indoor environment with different lightings and backgrounds. In addition, due to the relevance of performing evaluation experiments in such a way that a reliable comparison of the results can be accomplished, an evaluation protocol is provided at the end of this paper. Moreover, performance results are provided for several biometric traits in mono- and multi- modalities that can be used as a baseline.

A robustness verification system for mobile phone authentication based on gestures using Linear Discriminant Analysis

This article evaluates an authentication technique for mobiles based on gestures. Users create a remindful identifying gesture to be considered as their in-air signature. This work analyzes a database of 120 gestures of different vulnerability, obtaining an Equal Error Rate (EER) of 9.19% when robustness of gestures is not verified. Most of the errors in this EER come from very simple and easily forgeable gestures that should be discarded at enrollment phase. Therefore, an in-air signature robustness verification system using Linear Discriminant Analysis is proposed to infer automatically whether the gesture is secure or not. Different configurations have been tested obtaining a lowest EER of 4.01% when 45.02% of gestures were discarded, and an optimal compromise of EER of 4.82% when 19.19% of gestures were automatically rejected.

Architectures to Implement In-Air Signature Mobile Authentication to Increase the Security of E-Commerce Applications and Opinion of End Users

This article proposes to include an in-air signature biometric technique in mobile e-commerce applications in order to increase the security of passwords and authenticate users directly from their mobile phones. Five architectures have been proposed to implement this technique as a complement of current authentication methods. Moreover, the opinion of end users has been considered assessed by a survey of 24 people. From the results of this survey it has been deducted that this authentication method seems useful, necessary and comfortable to complement the password authentication methods and increase the global security of e-commerce applications.

Hand Biometrics in Mobile Devices

New trends in biometrics are inclined to adapt both identification and verification process to mobile devices in order to provide real scenarios and applications with a more secure frame. In fact, upcoming applications related to electronic commerce are demanding more trustworthy and reliable techniques to ensure their operations and transactions Van Thanh (2000), for instance. In other words, biometrics are requested to provide an appropriate alternative to current pin codes and passwords. Furthermore, commercial biometric systems normally have no constraints in terms of computational cost or involved hardware but they do aim the highest accuracy in personal identification. In contrast, applying biometrics to mobile devices requires a reconsideration of previous lack of constraints since a mobile device is at present far from being comparable to current biometric systems in terms of hardware. Based on these concerns, this document presents a biometric system based on hand geometry oriented to mobile devices, since hand images were acquired with mobile devices. This approach offers the possibility of identifying individuals easily with a non-intrusive acquisition procedure, using a picture taken with the mobile phone and avoiding the use of a flat surface to place the hand, providing this system with a non-contact characteristic. Moreover, the hand can be acquired without constraints in orientation, distance to camera or illumination, since the proposed technique within this paper is invariant to previous changes. This property provides an increase in the acceptance of the biometric technique by the final user, together with the fact that no removal of rings, watches and the like is required for image acquisition. In contrast, such lack of constraints in acquisition demands a more challenging solution in relation to segmentation and feature extraction. The former operation must be able to isolate completely hand from background, regardless what is behind the hand. In case of feature extraction, the template must be independent fromwhich hand is considered for identification (left or right hand) and invariant to changes in orientation, position, distance to camera and the like. In addition, the proposed template considers finger widths and lengths and, besides, information from four fingers (index, middle, ring and little/pinky) is considered, instead of global features from the whole hand. 18