We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated.Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized.A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail.The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.

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Abstract. For the recognition of continuous sign language we analyse whether we can improve the results by explicitly incorporating depth information. Accurate hand tracking for sign language recognition is made difficult by abrupt and fast changes in hand position and configuration, overlapping hands, or a hand signing in front of the face. In our system depth information is extracted using a stereo-vision method that considers the time axis by using pre-and succeeding frames. We demonstrate that depth information helps to disambiguate overlapping hands and thus to improve the tracking of the hands. However, the improved tracking has little influence on the final recognition results.

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