Ricerc@Sapienza

Technology is expected to enhance life in a Smart City: everything is intelligent, digital, interconnected, and inclusive. In addition, all everyday activities are facilitated. This paper presents a biometric authentication strategy based on gait dynamics. The produced signals are acquired by the common mobile device accelerometers (especially those embedded in smartphones). The user has nothing to do but normally approach a controlled entry: authentication is automatically triggered by ambient elements (beacons).

This paper describes a novel biometric architecture to implement unsupervised face recognition across varying demographics. The present proposal deals with ethnicity, gender and age, but the same strategy can be crafted for any mix of soft/hard biometrics, sensors, and/or methods. Our aim is not to explicitly distinguish demographic features of a subject (e.g., male vs. female). We rather aim at implicitly exploiting such information to improve the accuracy of subject identification. The role demographics plays in authentication has been reported by many recent studies.