Keystroke dynamics is a viable and practical way as an addition to security for identity veri cation. It can be combined with passphrases authentication resulting in a more secure veri cation sys- tem. This paper presents a new soft biometric approach for keystroke dynamics. Soft biometrics traits are physical, behavioral or adhered hu- man characteristics, which have been derived from the way human be- ings normally distinguish their peers (e.g. height, gender, hair color etc.). Those attributes have a low discriminating power, thus not capable of identi cation performance. Additionally, they are fully available to ev- eryone which makes them privacy-safe. Thus, in this study, it consists of extracting information from the keystroke dynamics templates with the ability to recognise the hand(s) used (i.e. one/two hand(s)); the gender; the age category; and the handedness of a user when he/she types a given password or passphrase on a keyboard. Experiments were conducted on a keystroke dynamics database of 110 users and our experimental results show that the proposed methods are promising.