| Summary: | Biometric recognition is an advanced technology that employs physical features (such as fingerprint, iris and face capture) and behavioural features (such as gait, signature and voice) to identify people. Biometric features are reliable and valid ways to describe the unique properties of individuals, but there are often rigorous requirements on the position and characteristics of devices used for data acquisition. Since biometric features can be difficult to capture at a distance, soft biometric features, such as height, weight, skin colour and gender, have received much attention. Although the uniqueness of soft biometric features is not as intuitively obvious as traditional biometric features, numerous experiments have demonstrated that the desired recognition accuracy can be achieved by using different soft biometric features. This thesis will propose state-of-the-art multimodal biometric fusion techniques to improve recognition performance of soft biometrics. The first contribution of this thesis is to estimate fusion performance based on three types of soft biometrics - face, body and clothing. Feature level and score level fusion strategies will be employed to measure and analyse the influence of fusion on soft biometric recognition. The second key contribution of this research is that the analysis of the influence of distance on soft biometric traits and an exploration of the potency of recognition using fusion at varying distances have been performed. A new soft biometric database, containing images of the human face, body and clothing taken at three different distances, was created and used to obtain face, body and clothing attributes. First, this new database was constructed to explore the suitability of each modality at a distance: intuitively, the face is suitable for near field identification, and the body becomes optimal when the subject is further away. The new dataset is used to explore the potential of face, body and clothing for human recognition using fusion. In this section, some novel fusion techniques on different levels (feature, score and rank level) are proposed to improve soft biometric recognition performance. A Supervised Generalised Canonical Correlation (SG-CCA) methodology is proposed to fuse the soft biometric features. The proposed SG-CCA is numerically validated to be the best fusion method compared with other multi-modal fusion methods. An SVM-weighted Likelihood Ratio Test (SVM-LRT) method is proposed for score level fusion. The experimental results demonstrate that SVM-LRT-based fusion significantly outperforms the single-mode recognition. A novel joint density distribution-based rank-score fusion is also proposed to combine rank and score information. Analysis using the new soft biometric database demonstrates that recognition performance is significantly improved by using the new methods over single modalities at different distances.
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