Offline Handwritten Signature Verification - Literature Review <s> Directional features <s> The first stage of a complete automatic handwritten signature verification system (AHSVS) is described in this paper. Since only random forgeries are taken into account in this first stage of decision, the directional probability density function (PDF) which is related to the overall shape of the handwritten signature has been taken into account as feature vector. Experimental results show that using both directional PDFs and the completely connected feedforward neural network classifier are valuable to build the first stage of a complete AHSVS. > <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Directional features <s> A neural network approach is proposed to build the first stage of an Automatic Handwritten Signature Verification System. The directional Probability Density Function was used as a global shape factor and its discriminating power was enhanced by reducing its cardinality via filtering. Various experimental protocols were used to implement the backpropagation network (BPN) classifier. A comparison, on the same database and with the same decision rule, shows that the BPN classifier is clearly better than the threshold classifier and compares favourably with the k-Nearest-Neighbour classifier. <s> BIB002 </s> Offline Handwritten Signature Verification - Literature Review <s> Directional features <s> Purpose – The purpose of this paper is to propose an effective method to perform off‐line signature verification and identification by applying a local shape descriptor pyramid histogram of oriented gradients (PHOGs), which represents local shape of an image by a histogram of edge orientations computed for each image sub‐region, quantized into a number of bins. Each bin in the PHOG histogram represents the number of edges that have orientations within a certain angular range.Design/methodology/approach – Automatic signature verification and identification are then studied in the general binary and multi‐class pattern classification framework, with five different common applied classifiers thoroughly compared.Findings – Simulation experiments show that PHOG has obvious advantages in the extraction of discriminating information from handwriting signature images compared with many previously proposed signature feature extraction approaches. The experiments also demonstrate that several classifiers, including... <s> BIB003 </s> Offline Handwritten Signature Verification - Literature Review <s> Directional features <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> BIB004
Directional features seek to describe the image in terms of the direction of the strokes in the signature. Sabourin BIB001 and Drouhard BIB002 extracted Directional-PDF (Probability Density Function) from the gradient of the signature outline (see Figure 7 ). Recent work from Rivard BIB004 used this method of feature extraction using grids of multiple scales, yielding promising results. Zhang et al. have investigate the usage of pyramid histogram of oriented gradients (PHOG) BIB003 . This descriptor represents local shapes in a image by a histogram of edge orientations, also in multiple scales (Figure 8 ). This strategy obtained state-of-the-art results on an experiment that used skilled forgeries for training.