We propose an extraction method of personal features based on on-line handwriting information. Most recent research has been focused on signature verification, especially in the field of on-line writer verification. However, signature verification has a serious problem in that it will accept forged handwriting. To solve this problem, we have introduced an on-line writer verification method which uses ordinary characters. In this method, any handwritten characters (i.e., ordinary characters) are accepted as a text in the verification process, and the text used in the verification process can be different from that in the enrollment process. However, in the proposed method, personal features are extracted only from the shape of strokes, and it is still uncertain how efficient other on-line information, such as writing pressure or pen inclination, is for extracting personal features. Therefore, we propose an extraction method of personal features based on on-line handwriting information, including writing-pressure and pen-inclination information. In the proposed method, handwriting information is described by a set of three-dimensional curves, and personal features are described by a set of Fourier descriptors for the three-dimensional curves. We also discuss the reliability of the proposed method with some simulation results using handwritten data. From these simulation results, it is clear that the proposed method effectively extracts personal features from ordinary characters.

We propose an on-line writer verification method to improve the reliability of verifying a specific system user. Most of the recent research focus on signature verification especially in the field of on-line writer verification. However, signature verification has a serious problem in that it will accept forged handwriting. To overcome this problem, we have introduced a text-indicated writer verification method. In this method, a different text including ordinary characters is used on every occasion of verification. This text can be selected automatically by the verification system so as to reflect the specific writer's personal features. A specific writer is accepted only when the same text as indicated by the verification system is inputted, and the system can verify the writer's personal features from the inputted text. Moreover, the characters used in the verification process can be different from those in the enrolment process. This method makes it more difficult to get away with forged handwriting than the previous methods using only signatures. We also discuss the reliability of the proposed method with some simulation results using handwriting data. From these simulation results, it is clear that this method keeps high reliability without the use of signatures.