A study of the ICP algorithm for recognition of the hand alphabet

Due to high demands of accuracy and efficiency while dealing with lots of complex and ambiguous gestures, the sign language recognition is still a hard problem to vision-based systems. From depth images acquired by a recent RGB-D sensor, this paper proposes a novel methodology for the recognition of the American Sign Language hand alphabet, which is underlain by a detailed analysis of the Iterative Closest Point (ICP) algorithm, applied here as a tridimensional shape matching procedure. The evaluation of the inputs and outputs of the ICP technique combined with the interpretation of the conducted experiments contribute significantly for advancing the state of the art, by identifying in which conditions the alignment can be used in compliance with the requirements of the pattern recognition.