FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP

9Probability of Collision10.90.80.70.60.50.40.30.20.10p(N) all traitsp(N) no glassesp(N) no skin colorp(N) no hair colorp(N) no eye colorq(N) all traitsq(N) no glassesq(N) no skin colorq(N) no hair colorq(N) no eye color2 4 6 8 10 12 14Subjects NFigure 1: Collision probability in an n sized authentication group.Furthermore we introduce the number of effective categories F which we identify as an importantparameter related to collision, and is shown to directly affect the overall performance ofan SBS. We analyze the statistical distribution and mean of F and furthermore offer an insightregarding the bounds of the statistical behavior ofF over large populations. These bounds addressthe following practical question: if more funds are spent towards increasing the quality of an SBS,then what reliability gains do we expect to see?We examine the influence of algorithmic estimation errors and give an example on the overallperformance of a realistic SBS. We improve the performance by a study of the distribution betweenpopulation in the overall categories. We then proceed to elaborate on the human compliant aspectof soft biometrics in re–identification, hereby specifically on the quantification of traits and on thehuman interaction view of an SBS.Search pruning in video surveillance systemsWe explore then the application using soft-biometric related categorization-based pruning tonarrow down a large search.In recent years we have experienced an increasing need to structure and organize an exponentiallyexpanding volume of images and videos. Crucial to this effort is the often computationallyexpensive task of algorithmic search for specific elements placed at unknown locations inside largedata sets. To limit computational cost, soft biometrics pruning can be used, to quickly eliminate aportion of the initial data, an action which is then followed by a more precise and complex searchwithin the smaller subset of the remaining data. Such pruning methods can substantially speedup the search, at the risk though of missing the target due to classification errors, thus reducingthe overall reliability. We are interested in analyzing this speed vs. reliability tradeoff, and wefocus on the realistic setting where the search is time-constrained and where, as we will see lateron, the environment in which the search takes place is stochastic, dynamically changing, and cancause search errors. In our setting a time constrained search seeks to identify a subject from alarge set of individuals. In this scenario, a set of subjects can be pruned by means of categorizationthat is based on different combinations of soft biometric traits such as facial color, shapes ormeasurements. We clarify that we limit our use of “pruning the search” to refer to the categoriza-