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By Anil K Jain

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Additional info for 3D object recognition: Representation and matching

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To characterize the head pose, two angles, yaw and tilt are used as basis coordinates in the feature space. The other basis coordinates represent discriminating feature patterns of faces. Based on recovered pose information, a trajectory of the input feature pattern can be constructed. The trajectories of features from known subjects arranged in the same temporal order can be synthesized on their respective identity surfaces. To recognize a face across views over time, the trajectory for the input face is matched to the trajectories synthesized for the known subjects.

The other basis coordinates represent discriminating feature patterns of faces. Based on recovered pose information, a trajectory of the input feature pattern can be constructed. The trajectories of features from known subjects arranged in the same temporal order can be synthesized on their respective identity surfaces. To recognize a face across views over time, the trajectory for the input face is matched to the trajectories synthesized for the known subjects. This approach can be thought of as a generalized version of face recognition based on single images taken at different poses.

Minimizing E = − ln(P (Iinput | α, β, ρ) p(α, β, ρ)). A stochastic gradient algorithm is used to obtain the optimum solution. In the experiments, synthetic images of size 58 × 58 are generated for training both the detector and the classifier. Specifically, the faces were rotated in depth from 0◦ to 34◦ in 2◦ increments and rendered with two illumination models (the first model consists of ambient light alone and the second includes ambient light and a rotating point light source) at each pose.

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