Through the HUMMINGBIRD framework outlined here, we seek to encourage a novel multidisciplinary approach to biometric analysis with the goal of enhancing both understanding and accuracy of identification.

Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices.

The capacity to link various aspects of a person's identity is of value in the search for reliable means of authentication and identification. With the increase in digital living, this has taken on a new perspective through the need to link aspects of identity in the physical world to those in the digital world. The focus in this work is in the value of the signature as a token of identity in its own right but also as a method to reveal information about the person signing. Whilst existing methods for the analysis of an individual's personality through their handwriting (graphology) have been discredited, we wish to revisit the issue with respect to signatures. Critically, we use accepted and modern static and dynamic features from the signature as potential indicators of personality. Our results suggest some clear links between signature production and relevant cues about the signer, especially when we incorporate dynamic elements of signature production. As such these results suggest there is renewed value in using a signature to reveal information about the signer.

Soft biometrics are a new form of biometric identification which use physical or behavioral traits that can be naturally described by humans. Unlike other biometric approaches, this allows identification based solely on verbal descriptions, bridging the semantic gap between biometrics and human description. To permit soft biometric identification the description must be accurate, yet conventional human descriptions comprising of absolute labels and estimations are often unreliable. A novel method of obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe differences between subjects. This innovative approach has been shown to address many problems associated with absolute categorical labels-most critically, the descriptions contain more objective information and have increased discriminatory capabilities. Relative measurements of the subjects' traits can be inferred from comparative human descriptions using the Elo rating system. The resulting soft biometric signatures have been demonstrated to be robust and allow accurate recognition of subjects. Relative measurements can also be obtained from other forms of human representation. This is demonstrated using a support vector machine to determine relative measurements from gait biometric signatures-allowing retrieval of subjects from video footage by using human comparisons, bridging the semantic gap.