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 victim's PIN but has no information about it's drawing characteristic and 2) Imitation attack where an attacker has access to a dynamic drawing sequence of a victim's 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.

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.

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.

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.

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.