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Soft biometrics have been recently proposed for improving the verification performance of biometric recognition systems. Examples of soft biometrics are skin, eyes, hair colour, height, and ethnicity. Some of them are often cheaper than ''hard'', standard biometrics (e.g., face and fingerprints) to extract. They exhibit a low discriminant power for recognizing persons, but can add some evidences about the personal identity, and can be useful for a particular set of users. In particular, it is possible to argue that users with a certain high discriminant soft biometric can be better recognized. Identifying such users could be useful in exploiting soft biometrics at the best, as deriving an appropriate methodology for embedding soft-biometric information into the score computed by the main biometric. In this paper, we propose a group-specific algorithm to exploit soft-biometric information in a biometric verification system. Our proposal is exemplified using hair colour and ethnicity as soft biometrics and face as biometric. Hair colour and information about ethnicity can be easily extracted from face images, and used only for a small number of users with highly discriminant hair colour or ethnicity. We show by experiments that for those users, hair colour or ethnicity strongly contributes to reduce the false rejection rate without a significant impact on the false acceptance rate, whilst the performance does not change for other users.