A MULTI-STEP ALIGNMENT SCHEME FOR FACE RECOGNITION IN RANGE IMAGES

Face Recognition in range images is a challenging task, especially if the pose of the shown face is unknown. To solve this, an alignment procedure consisting of facial feature hypotheses extraction by invariant curvature features, PCA-based classification and Iterative Closest Point alignment will be introduced to create aligned and normalized patches. These patches will then be used in a recognition algorithm, a discrete Pseudo 2-Dimensional Hidden Markov Model approach based on vector quantized DCTmod2 features. The results of this processing chain are discussed and compared to previous works.