Napa Sae-Bae

Biometric-rich gestures: a novel approach to authentication on multi-touch devices

In this paper, we present a novel multi-touch gesture-based authentication technique. We take advantage of the multi-touch surface to combine biometric techniques with gestural input. We defined a comprehensive set of five-finger touch gestures, based upon classifying movement characteristics of the center of the palm and fingertips, and tested them in a user study combining biometric data collection with usability questions. Using pattern recognition techniques, we built a classifier to recognize unique biometric gesture characteristics of an individual. We achieved a 90% accuracy rate with single gestures, and saw significant improvement when multiple gestures were performed in sequence. We found user ratings of a gestures desirable characteristics (ease, pleasure, excitement) correlated with a gestures actual biometric recognition rate - that is to say, user ratings aligned well with gestural security, in contrast to typical text-based passwords. Based on these results, we conclude that multi-touch gestures show great promise as an authentication mechanism.

Online Signature Verification on Mobile Devices

This paper studies online signature verification on touch interface-based mobile devices. A simple and effective method for signature verification is developed. An online signature is represented with a discriminative feature vector derived from attributes of several histograms that can be computed in linear time. The resulting signature template is compact and requires constant space. The algorithm was first tested on the well-known MCYT-100 and SUSIG data sets. The results show that the performance of the proposed technique is comparable and often superior to state-of-the-art algorithms despite its simplicity and efficiency. In order to test the proposed method on finger drawn signatures on touch devices, a data set was collected from an uncontrolled environment and over multiple sessions. Experimental results on this data set confirm the effectiveness of the proposed algorithm in mobile settings. The results demonstrate the problem of within-user variation of signatures across multiple sessions and the effectiveness of cross session training strategies to alleviate these problems.

Multitouch Gesture-Based Authentication

This paper investigates multitouch gestures for user authentication on touch sensitive devices. A canonical set of 22 multitouch gestures was defined using characteristics of hand and finger movement. Then, a multitouch gesture matching algorithm robust to orientation and translation was developed. Two different studies were performed to evaluate the concept. First, a single session experiment was performed in order to explore feasibility of multitouch gestures for user authentication. Testing on the canonical set showed that the system could achieve good performance in terms of distinguishing between gestures performed by different users. In addition, the tests demonstrated a desirable alignment of usability and security as gestures that were more secure from a biometric point of view were rated as more desirable in terms of ease, pleasure, and excitement. Second, a study involving a three-session experiment was performed. Results indicate that biometric information gleaned from a short user-device interaction remains consistent across gaps of several days, though there is noticeable degradation of performance when the authentication is performed over multiple sessions. In addition, the study showed that user-defined gestures yield the highest recognition rate among all other gestures, whereas the use of multiple gestures in a sequence aids in boosting verification accuracy. In terms of memorability, the study showed that it is feasible for a user to recall user-defined gestural passwords and it is observed that the recall rate increases over time. It is also noticed that performing a user-defined gesture over a customized background image does result in higher verification performance. In terms of usability, the study shows that users did not have difficulty in performing multitouch gestures as they all rated each gesture as easy to perform.

Investigating multi-touch gestures as a novel biometric modality

We propose a new behavioral biometric modality based on multi-touch gestures. We define a canonical set of multi-touch gestures based on the movement characteristics of the palm and fingertips being used to perform the gesture. We developed an algorithm to generate and verify multi-touch gesture templates. We tested our techniques on a set of 22 different gestures. Employing a matching algorithm for a multi-touch verification system with a k-NN classifier we achieved 1.28% Equal Error Rate (EER). With score-based classifiers where only the first five samples of a genuine subject were considered as templates, we achieved 4.46 % EER. Further, with the combination of three commonly used gestures: pinch, zoom, and rotate, using all five fingers, 1.58% EER was achieved using a score-based classifier. These results are encouraging and point to the possibility of touch based biometric systems in real world applications like user verification and active authentication.

Towards automatic detection of child pornography

This paper presents a child pornographic image detection system that identifies human skin tones in digital images, extracts features to detect explicit images and performs facial image based age classification. The novelty of the technique relies on the use of a robust and very fast skin color filter and a new set of facial features for improved identification of child faces. Tests on a dataset containing explicit images taken under different illuminations and reflecting a diversity of human skin tones, show that explicit images can be differentiated from benign images with around 90% accuracy. Similarly, tests performed on adult and child facial images yielded an accuracy of 80% in detecting child faces. Test conducted on 105 images involving semi-naked children (with no sexual context) revealed that the system has true positive rates of 83% in detecting explicit-like images and 96.5% in detecting child faces.

Finger-drawn pin authentication on touch devices

PIN authentication is widely used thanks to its simplicity and usability, but it is known to be susceptible to shoulder surfing. In this paper, we propose a novel online finger-drawn PIN authentication technique that lets a user draw a PIN on a touch interface with her finger. The system provides some resilience to shoulder surfing without increasing authentication delay and complexity by using both the PIN as well as a behavioral biometric in user verification. Our approach adopts the Dynamic Time Warping (DTW) algorithm to compute dissimilarity scores between PIN samples. We evaluate our system in two shoulder surfing scenarios: 1) PIN attack where the attacker only knows the victims PIN but has no information about its drawing characteristic and 2) Imitation attack where an attacker has access to a dynamic drawing sequence of a victims finger-drawn PIN in the form of multiple observations. Experimental results with a data set of 40 users and 2400 imitating samples from two attacks yield an Equal Error Rate (EER) of 6.7% and 9.9% respectively, indicating the need for further study on this promising authentication mechanism.

DRAW-A-PIN

This paper presents Draw-A-PIN, a user authentication system on a device with a touch interface that supports the use of PINs. In the proposed system, the user is asked to draw her PIN on the touch screen instead of typing it on a keypad. Consequently, Draw-A-PIN could offer better security by utilizing drawing traits or behavioral biometrics as an additional authentication factor beyond just the secrecy of the PIN. In addition, Draw-A-PIN inherently provides acceptability and usability by leveraging user familiarity with PINs. To evaluate the security and usability of the approach, Draw-A-PIN was implemented on Android phones and 3203 legitimate finger-drawn PINs and 4655 forgery samples were collected through an extensive and unsupervised field experiment over 10 consecutive days. Experimental results show that Draw-A-PIN achieves an equal error rate of 4.84% in a scenario where the attacker already knows the PIN by shoulder surfing. Finally, results from a user study based on the System Usability Scale questionnaire confirm that Draw-A-PIN is highly usable.

Hand authentication on multi-touch tablets

We propose a biometric authentication scheme suitable for multi-touch devices such as tablet computers. Our scheme is based on hand geometry. It improves on prior work by introducing a dynamic element, where movement challenges are issued based on static hand geometry data. Specifically, we demonstrate a set of multi-touch interactions that can capture hand geometry information of users. For each of the interactions, we extract different but complimentary hand geometric information from the user. Our approach has several advantages over traditional text password and other biometric authentications. First, unlike other recognition based authentication schemes, a user is only expected to interact with the multi-touch surface according to the challenges she is posed. In other words, she does not have to memorize any type of credential. In addition, the system provides security against replay attacks--which is a drawback associated with many authentication schemes, such as traditional biometric system based on recognition of face, iris or fingerprint. Last but not least, our approach works on current tablet computers without any needs for updates of hardware, firmware or drivers -- it can be carried out by an application. We demonstrate experimentally a configuration using 14 consecutive challenges on iPad2 tablet (taking approximately 3.32 minutes for novice users to respond to), wherein the user is authenticated with almost 97% accuracy.

Distinctiveness, complexity, and repeatability of online signature templates

Abstract This paper proposes three measures to quantify the characteristics of online signature templates in terms of distinctiveness, complexity and repeatability. A distinctiveness measure of a signature template is computed from a set of enrolled signature samples and a statistical assumption about random signatures. Secondly, a complexity measure of the template is derived from a set of enrolled signature samples. Finally, given a signature template, a measure to quantify the repeatability of the online signature is derived from a validation set of samples. These three measures can then be used as an indicator for the performance of the system in rejecting random forgery samples and skilled forgery samples and the performance of users in providing accepted genuine samples, respectively. The effectiveness of these three measures and their applications are demonstrated through experiments performed on three online signature datasets and one keystroke dynamics dataset using different verification algorithms.

Noise estimation for denoising multi-frame color images

This paper proposes and analyzes the performance of noise estimation technique for multi-frame noisy images. The proposed method is then applied to denoise multi-frame color image that contain different level of image contents or activity levels. The evaluation results have shown that, for the image with high level of activity, i.e., the one with large high frequency components, our technique outperforms many conventional image denoising approaches.