Koichiro Niinuma

Soft Biometric Traits for Continuous User Authentication

Most existing computer and network systems authenticate a user only at the initial login session. This could be a critical security weakness, especially for high-security systems because it enables an impostor to access the system resources until the initial user logs out. This situation is encountered when the logged in user takes a short break without logging out or an impostor coerces the valid user to allow access to the system. To address this security flaw, we propose a continuous authentication scheme that continuously monitors and authenticates the logged in user. Previous methods for continuous authentication primarily used hard biometric traits, specifically fingerprint and face to continuously authenticate the initial logged in user. However, the use of these biometric traits is not only inconvenient to the user, but is also not always feasible due to the users posture in front of the sensor. To mitigate this problem, we propose a new framework for continuous user authentication that primarily uses soft biometric traits (e.g., color of users clothing and facial skin). The proposed framework automatically registers (enrolls) soft biometric traits every time the user logs in and fuses soft biometric matching with the conventional authentication schemes, namely password and face biometric. The proposed scheme has high tolerance to the users posture in front of the computer system. Experimental results show the effectiveness of the proposed method for continuous user authentication.

Automatic multi-view face recognition via 3D model based pose regularization

One of the major challenges encountered by face recognition lies in the difficulty of handling arbitrary poses variations. While different approaches have been developed for face recognition across pose variations, many methods either require manual landmark annotations or assume the face poses to be known. These constraints prevent many face recognition systems from working automatically. In this paper, we propose a fully automatic method for multiview face recognition. We first build a 3D model from each frontal target face image, which is used to generate synthetic target face images. The pose of a query face image is also estimated using a multi-view face detector so that the synthetic target face images can be generated to resemble the pose variation of a query face image. Procrustes analysis is then applied to align the synthetic target images and the query image, and block based MLBP features are extracted for face matching. Experimental results on two public-domain databases (Color FERET and PubFig), and a Mobile face database collected using mobile phones show that the proposed approach outperforms two state-of-the-art face matchers (FaceVACS and MKD-SRC) in automatic multi-view face recognition. The proposed approach can also be easily extended to leverage existing face recognition systems for automatic multi-view face recognition.

Erratum to “Soft Biometric Traits for Continuous User Authentication” [Dec 10 771-780]

In the Acknowledgment for the above paper (ibid., vol. 5, no. 4, pp. 771-780, Dec 2010), due to a production error, the corresponding authors name was spelled incorrectly. The correct spelling is Unsang Park.