On the effectiveness of soft biometrics for increasing face verification rates

The term soft biometrics typically refers to attributes of people such as their gender, the shape of their head or the color of their hair. There is growing interest in soft biometrics as a means of improving automated face recognition since they hold the promise of significantly reducing recognition errors, in part by ruling out illogical choices. This paper concentrates specifically on soft biometrics as opposed to extended attributes, and presents the results from three experiments quantifying performance gains on a difficult face recognition task when standard face recognition algorithms are augmented using soft biometrics. These experiments include (1) a best-case analysis using perfect knowledge of gender and race, (2) support vector machine-based soft biometric classifiers and (3) face shape expressed through an active shape model. All three experiments indicate small improvements may be made when soft biometrics augment an existing algorithm. However, in all cases, the gains were modest. One reason is that false matches are more likely between faces of people sharing the same soft biometric traits. This is to be expected, since face recognition algorithms utilize appearance information, which is the same information used by algorithms designed to assign soft biometric labels to face images.