HUMAN FACE PERCEPTION IN DEGRADED IMAGES

This paper reports human performance data from a series of psychophysi cal experiments investigating the limits of stimulus parameters releva nt to distinguishing a human face in a mug shot, In these experiments, we use a two-alternative forced-choice paradigm for response elicitat ion, We develop a benchmark that can be used to determine the performa nce of a machine vision system for human face detection at different l evels of image degradation. The benchmark is developed in terms of the number of pixel blocks and the number of gray scales used in the imag es, The paper presents a model of representation that can be useful fo r recognition of faces in a database, and may be used to define the mi nimum image quality required for retrieval of facial records at differ ent confidence levels. Our results show that low-frequency information in face images is useful since it is most resilient to degradation in the image quality, The model is particularly relevant to the retrieva l of facial images in large image databases, (C) 1995 Academic Press, Inc.