Fusing with context: A Bayesian approach to combining descriptive attributes

For identity related problems, descriptive attributes can take the form of any information that helps represent an individual, including age data, describable visual attributes, and contextual data. With a rich set of descriptive attributes, it is possible to enhance the base matching accuracy of a traditional face identification system through intelligent score weighting. If we can factor any attribute differences between people into our match score calculation, we can deemphasize incorrect results, and ideally lift the correct matching record to a higher rank position. Naturally, the presence of all descriptive attributes during a match instance cannot be expected, especially when considering non-biometric context. Thus, in this paper, we examine the application of Bayesian Attribute Networks to combine descriptive attributes and produce accurate weighting factors to apply to match scores from face recognition systems based on incomplete observations made at match time. We also examine the pragmatic concerns of attribute network creation, and introduce a Noisy-OR formulation for streamlined truth value assignment and more accurate weighting. Experimental results show that incorporating descriptive attributes into the matching process significantly enhances face identification over the baseline by up to 32.8%.