Semantic Face Signatures: Recognizing and Retrieving Faces by Verbal Descriptions

The adverse visual conditions of surveillance environments and the need to identify humans at a distance have stimulated research in soft biometric attributes. These attributes can be used to describe a human’s physical traits semantically and can be acquired without their cooperation. Soft biometrics can also be employed to retrieve identity from a database using verbal descriptions of suspects. In this paper, we explore unconstrained human face identification with semantic face attributes derived automatically from images. The process uses a deformable face model with keypoint localisation which is aligned with attributes derived from semantic descriptions. Our new framework exploits the semantic feature space to infer face signatures from images and bridges the semantic gap between humans and machines with respect to face attributes. We use an unconstrained dataset, LFW-MS4, consisting of all the subjects from view-1 of the LFW database that have four or more samples. Our new approach demonstrates that retrieval via estimated comparative facial soft biometrics yields a match in the top 10.23% of returned subjects. Furthermore, modelling of face image features in the semantic space can achieve an equal error rate of 12.71%. These results reveal the latent benefits of modelling visual facial features in a semantic space. Moreover, they highlight the potential of using images and verbal descriptions to generate comparative soft biometrics for subject identification and retrieval.