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121] on a dataset including 29 subjects. Other than the KL divergence, probabilistic distance measures such as Chernofif distance and Bhattacharyya distance can also be used. 2]~ {pi - H2) , 1 |aiSi +a2S2| log2 ° |Ei|«i|I]2|«2 where a i > 0, a2 > 0 and a i + 02 = 1. When ai— a2 — 1/2, the Chernoff distance reduces to the Bhattacharyya distance. P2) = Ip\{^)pl{x)dx, r > 0. 21) •Jx When r = 1/2, the kernel function k reduces to the so-called Bhattacharyya kernel since it is related to the Bhattacharyya distance.

P2: Approaches utilizing temporal continuity/dynamics Simultaneous tracking and recognition is an approach proposed by Zhou et al. [129] that systematically studied how to incorporate temporal continuity in video-based recognition. Zhou et al. modeled two tasks involved, namely tracking and recognition, in a probabilistic framework. e. the video frame). The time series model is fully specified by the state transition probability/)(nt, 9t\nt-i,9t-i) and the observational likelihood/j(yf|^t, nt).

Li and Chellappa [112] also proposed an approach somewhat similar to [129]. In [112], only tracking was implemented using SIS and recognition scores were subsequently derived based on tracking results. Lee et al. [109] performed video-based face recognition using probabilistic appearance manifolds. , a generic appearance manifold consists of several pose manifolds. Since each pose manifold is represented using a linear subspace, the overall appearance manifold is approximated by piecewise linear subspaces.

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Access 2007 by John Hales

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