Quality-dependent Score-level Fusion of Face, Gait, and the Height Biometrics

This paper describes a quality-dependent score-level fusion framework of face, gait, and the height biometrics from a single walking image sequence. Individual person authentication accuracies by face, gait, and the height biometrics, are in general degraded when spatial resolution (image size) and temporal resolution (frame-rate) of the input image sequence decrease and the degree of such accuracy degradation differs among the individual modalities. We therefore set the optimal weights of the individual modalities based on linear logistic regression framework depending on a pair of the spatial and temporal resolutions, which are called qualities in this paper. On the other hand, it is not a realistic solution to compute and store the optimal weights for all the possible qualities in advance, and also the optimal weights change across the qualities in a nonlinear way. We thus propose a method to estimate the optimal weights for arbitrary qualities from a limited training pairs of the optimal weights and the qualities, based on Gaussian process regression with a nonlinear kernel function. Experiments using a publicly available large population gait database with 1,935 subjects under various qualities, showed that the person authentication accuracy improved by successfully estimating the weights depending on the qualities.