Signature authentication systems often have to focus their processing on acquired dynamic and/or static signatures descriptors to authenticate persons. This approach gives satisfactory results in ordinary cases but remains vulnerable against skilled forgeries. This is mainly because there is no relation between the signatory and his signature. We will show that the inclusion of the hand shape in the authentication process will considerably reduce the false acceptance rates of skilled forgeries and improve the authentication accuracy performances. A new online hand signature authentication approach based on both signature and hand shape descriptor is proposed. The signature acquisition is completely transparent, which allows a high level of security against fraudulent imitation attempts. Authentication performances are evaluated with extensive experiments. The obtained test results [equal error rate (EER)=2%, genuine acceptance rate (GAR)=96%]confirm the efficiency of the proposed approach.
We propose a unified approach to propagate knowledge into a high-dimensional space from a small informative set, in this case, scale invariant feature transform (SIFT) features. Our contribution lies in three aspects. First, we propose a spectral graph embedding of the SIFT points for dimensionality reduction, which provides efficient keypoints transcription into a Euclidean manifold. We use iterative deflation to speed up the eigendecomposition of the underlying Laplacian matrix of the embedded graph. Then, we describe a variational framework for manifold denoising based on p -Laplacian to enhance keypoints classification, thereby lessening the negative impact of outliers onto our variational shape framework and achieving higher classification accuracy through agglomerative categorization. Finally, we describe our algorithm for multilabel diffusion on graph. Theoretical analysis of the algorithm is developed along with the corresponding connections with other methods. Tests have been conducted on a collection of images from the Berkeley database. Performance evaluation results show that our framework allows us to efficiently propagate the prior knowledge.
We present a nonconstraining and low-cost online signature acquisition system that has been developed to enhance the performances of an existing multimodal biometric authentication system (based initially on both voice and image modalities). A laboratory prototype has been developed and validated for an online signature acquisition.