Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Nearest neighbour approach in the least-squares data imputation algorithms
Information Sciences: an International Journal
Soft biometrics-combining body weight and fat measurements with fingerprint biometrics
Pattern Recognition Letters
Performing Content-Based Retrieval of Humans Using Gait Biometrics
SAMT '08 Proceedings of the 3rd International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
Second ACM international workshop on multimedia in forensics, security and intelligence (MiFor 2010)
Proceedings of the international conference on Multimedia
Identifying people with soft-biometrics at fleet week
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
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Human identification at a distance has received significant interest due to the ever increasing surveillance infrastructure. Biometrics such as face and gait offer a suitable physical attribute to uniquely identify people from a distance. When linking this with human perception, these biometrics suffer from the semantic gap which is the difference between how people and how biometrics represent and describe humans. Semantic biometrics bridges this gap, allowing conversions between gait biometrics and semantic descriptions. One possible application of semantic biometrics is to automatically search surveillance footage for a person who best matches a given semantic description - possibly obtained from an eyewitness report. We now exploit patterns and structure within the physical descriptions to be able to predict occluded or erroneous data, thereby widening application potential. We show how imputation techniques can be used to increase accuracy and robustness of automatic semantic annotation of gait signatures.