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An evaluation of multimodal 2D+3D face biometrics

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3 Author(s)
Chang, K.I. ; Dept. of Comput. Sci. & Eng., Notre Dame Univ., USA ; Bowyer, K.W. ; Flynn, P.J.

We report on the largest experimental study to date in multimodal 2D+3D face recognition, involving 198 persons in the gallery and either 198 or 670 time-lapse probe images. PCA-based methods are used separately for each modality and match scores in the separate face spaces are combined for multimodal recognition. Major conclusions are: 1) 2D and 3D have similar recognition performance when considered individually, 2) combining 2D and 3D results using a simple weighting scheme outperforms either 2D or 3D alone, 3) combining results from two or more 2D images using a similar weighting scheme also outperforms a single 2D image, and 4) combined 2D+3D outperforms the multi-image 2D result. This is the first (so far, only) work to present such an experimental control to substantiate multimodal performance improvement.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:27 ,  Issue: 4 )