Abstract
Soft biometrics are already widely used as a support tool for user identification. However, it is not the only use for biometric information that is conceivable because such information can be sufficient to obtain minimal details from the user that are unrelated to his identity. Examples of what might be referred to as soft biometrics include gender, hand orientation, and emotional state. Utilizing physiologic modalities for soft-biometric work is extremely prevalent, prediction, but behavioral data is frequently disregarded. Keystroke dynamics and handwriting signature are two potential behavioral modalities that could be used to predict a user's gender, but they are rarely discussed in the literature together. This study seeks to fill this gap by examining the influence of combining these two distinct biometric modalities on the accuracy of gender prediction and the best way. Key Words: Item key-strokes, Bio-metric signatures, digital signs, dynamic temporal wrapping (DTW)
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