Suspect identification based on descriptive facial attributes

We present a method for using human describable face attributes to perform face identification in criminal inves-tigations. To enable this approach, a set of 46 facial at-tributes were carefully defined with the goal of capturing all describable and persistent facial features. Using crowd sourced labor, a large corpus of face images were manually annotated with the proposed attributes. In turn, we train an automated attribute extraction algorithm to encode target repositories with the attribute information. Attribute extrac-tion is performed using localized face components to im-prove the extraction accuracy. Experiments are conducted to compare the use of attribute feature information, derived from crowd workers, to face sketch information, drawn by expert artists. In addition to removing the dependence on expert artists, the proposed method complements sketch-based face recognition by allowing investigators to imme-diately search face repositories without the time delay that is incurred due to sketch generation. 1