Consumers and marketers use facial information to make important inferences about others in many business contexts. However, consumers and firms are increasingly concerned about privacy and discrimination. To address privacy-perception trade-offs, the authors propose a novel contour-as-face (CaF) framework that transforms face images into contour images incorporating both the non-outline and outline features of facial parts. In three empirical studies, the authors: (a) compare human perceptions of face and contour images along 15 dimensions commonly assessed in marketing contexts; (b) investigate the effectiveness of contour images for protecting anonymity related to identity, age, and gender; and (c) implement the CaF framework in a real-life online dating context. Results show that the CaF framework effectively resolves privacy–perception trade-off problems by preserving the information that is useful for humans to make inferences about many relevant perceptual dimensions in marketing, while making it virtually impossible for humans to infer identity and very difficult to infer age and gender accurately; two critical discrimination factors. Results from the field implementation demonstrate the feasibility and value of using the CaF framework for real-life decision-making.