Fast on-line signature recognition based on VQ with time modeling
This work addresses efficient and privacy-friendly signature verification for biometric security applications, representing a strong specific gain.
The paper tackles on-line signature recognition by proposing a multi-section vector quantization approach, achieving a 99.76% identification rate and 2.46% EER on the MCYT database and outperforming the SVC winner with 47 times lower computational cost than DTW.
This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used the MCYT database, which consists of 330 users and 25 skilled forgeries per person performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system outperforms the winner of SVC with a reduced computational requirement, which is around 47 times lower than DTW. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly as it is not possible to recover the original signature using the codebooks. Experimental results with MCYT provide a 99.76% identification rate and 2.46% EER (skilled forgeries and individual threshold). Experimental results with SVC are 100% of identification rate and 0% (individual threshold) and 0.31% (general threshold) when using a two-section VQ approach.