Object Recognition Using Statistical Modeling

We are developing a human face detector and an automobile detector. Our method for both off these problems is based on a statistical decision model involving the statistics of over 100,000 patterns. We gather statistics of two probability distributions: the joint distribution of pattern and location on the object, P(pattern, x, y | object), and the joint distribution of pattern and location for the rest of world, P(pattern, x, y | non-object). Since pattern, x, and y take on a finite set of values, we collect each set of statistics by using a multidimensional histogram. We collect the histogram P(pattern, x, y | object) from a representative set of images of the object. Similarly, we collect P(pattern, x, y | non-object) from a representative set of images that do not contain the object. We then use these probability distributions to classify image regions as “object” or “non-object” by applying Bayes decision rule. With this approach, we have developed the most accurate frontal face detector currently in existence.