Segmented face approximation with adaptive region growing based on low-degree polynomial fitting

Nowadays, an objective in visual communication is to send and store images of faces at a low bit rate, such that the faces are still recognizable and that the compression does not prevent remote face analysis. We present a novel segmented face approximation algorithm. Greyscale face images are segmented into meaningful surface segments with an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L-infinity fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main novelty is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way, such as flat, planar, convex, concave or saddle patches. As a result, the surface segments correspond to facial features and the contours separating the surface segments coincide with real image face edges. Moreover, the curvature-based surface shape information facilitates many tasks in automated face analysis, demonstrated in this paper by face verification performed on the polynomial representation. The polynomial representation provides good approximation of facial features, while preserving all the necessary details of the face in the reconstructed image. When compared with different compression methods, we achieve higher compression ratios and better recognizable faces at low bit rates. This is confirmed by correct identification percentages obtained by face recognition algorithms on the compressed data.

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