Offline Handwritten Signature Verification - Literature Review <s> Feature selection <s> Some of the fundamental problems faced in the design of signature verification (SV) systems include the potentially large number of input features and users, the limited number of reference signatures for training, the high intra-personal variability among signatures, and the lack of forgeries as counterexamples. In this paper, a new approach for feature selection is proposed for writer-independent (WI) off-line SV. First, one or more preexisting techniques are employed to extract features at different scales. Multiple feature extraction increases the diversity of information produced from signature images, allowing to produce signature representations that mitigate intra-personal variability. Dichotomy transformation is then applied in the resulting feature space to allow for WI classification. This alleviates the challenges of designing off-line SV systems with a limited number of reference signatures from a large number of users. Finally, boosting feature selection is used to design low-cost classifiers that automatically select relevant features while training. Using this global WI feature selection approach allows to explore and select from large feature sets based on knowledge of a population of users. Experiments performed with real-world SV data comprised of random, simple, and skilled forgeries indicate that the proposed approach provides a high level of performance when extended shadow code and directional probability density function features are extracted at multiple scales. Comparing simulation results to those of off-line SV systems found in literature confirms the viability of the new approach, even when few reference signatures are available. Moreover, it provides an efficient framework for designing a wide range of biometric systems from limited samples with few or no counterexamples, but where new training samples emerge during operations. <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Feature selection <s> Standard signature verification (SV) systems are writer-dependent (WD), where a specific classifier is designed for each individual. It is inconvenient to ask a user to provide enough number of signature samples to design his WD classifier. In practice, very few samples are collected and inaccurate classifiers maybe produced. To overcome this, writer-independent (WI) systems are introduced. A global classifier is designed using a development database, prior to enrolling users to the system. For these systems, signature templates are needed for verification, and the template databases can be compromised. Moreover, state-of-the-art WI and WD systems provide enhanced accuracy through information fusion at either feature, score or decision levels, but they increase computational complexity. In this study, a hybrid WI-WD system is proposed, as a compromise of the two approaches. When a user is enrolled to the system, a WI classifier is used to verify his queries. During operation, user samples are collected and adapt the WI classifier to his signatures. Once adapted, the resulting WD classifier replaces the WI classifier for this user. Simulations on the Brazilian and the GPDS signature databases indicate that the proposed hybrid system provides comparative accuracy as complex WI and WD systems, while decreases the classification complexity. <s> BIB002
Rivard et al. BIB001 and Eskander et al. BIB002 have used a feature selection approach for signature verification. Rivard et al. trained a writer-independent classifier, by first extracting a large number of features from each signature (over 30 thousand features), applying feature extractors at different scales of the image. A Boosting Feature Selection (BFS) approach was then used to simultaneously select features and train a classifier. Their method consisted in training an ensemble of decision stumps (equivalent to a decision tree with only one node), where each decision stump only used one feature. With this approach, they were able to obtain a smaller feature representation (less than a thousand features) that achieved good results in the Brazilian and GPDS datasets. Eskander et al. BIB002 extended Rivard's work to train a hybrid writer-independent-writer-dependent classifier, by first training this writer-independent classifier to perform feature selection, and then train writerdependent classifiers using only the features that were selected by the first model. This strategy presented good results when a certain number of samples per user is reached.