With inspiration from psychophysical researches of the human visual system we propose a novel method for performance evaluation of contour based shape recognition algorithms. We use complete contour representations of objects as a training set. Incomplete contour representations of the same objects are used as a test set and the recognition performance of two shape based methods is investigated. The amount of incompleteness in test cases is quantified using the percentage of contour pixels retained. The performances of the methods are reported using the recognition rate as a function of the degree of incompleteness. We consider three types of incomplete contour representations, viz. segment-wise deletion, occlusion and random pixel depletion. The methods compared in this framework use shape context and distance multiset as local shape descriptors. Qualitatively, both methods mimic human visual perception in the sense that they perform best in the case of random depletion and worst in the case of occluded contours. Quantitatively, the distance multiset method performs better than the shape context method in this test framework.