We propose formulations for learning features for Offline Signature Verification.A novel method that uses knowledge of forgeries from a subset of users is proposed.Learned features are used to train classifiers for other users (without forgeries).Experiments on GPDS-960 show a large improvement in state-of-the-art.Results in other 3 datasets show that the features generalize without fine-tuning. Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a persons signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This reflects in a relatively poor performance, with verification errors around 7 in the best systems in the literature. To address both the difficulty of obtaining good features, as well as improve system performance, we propose learning the representations from signature images, in a Writer-Independent format, using Convolutional Neural Networks. In particular, we propose a novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user. Extensive experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-160, we obtained a large improvement in state-of-the-art performance, achieving 1.72 Equal Error Rate, compared to 6.97 in the literature. We also verified that the features generalize beyond the GPDS dataset, surpassing the state-of-the-art performance in the other datasets, without requiring the representation to be fine-tuned to each particular dataset.
A large variety of feature extractors have been investigated for this problem, from simple geometric descriptors @cite_26 , @cite_0 , descriptors inspired in graphology and graphometry @cite_30 , directional-based descriptors such as HOG @cite_48 and D-PDF @cite_35 , @cite_20 , @cite_19 , descriptors based on interest-point, such as SIFT @cite_48 , to texture descriptors, such as Local Binary Patterns (LBP) @cite_48 and Gray-Level Co-occurrence Matrix (GLCM) @cite_14 . These features are commonly extracted locally from the signature images, by dividing the image in a grid and computing descriptors for each cell (either in Cartesian or polar coordinates).
@cite_30 @cite_35 @cite_26 @cite_14 @cite_48 @cite_0 @cite_19 @cite_20
2172196609 1975959625 1975056068 2004134605
This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT. This paper presents a novel approach for texture classification, generalizing the well-known local binary pattern (LBP) approach. In the proposed approach, two different and complementary types of features (pixel intensities and differences) are extracted from local patches. The intensity-based features consider the intensity of the central pixel (CI) and those of its neighbors (NI); while for the difference-based feature, two components are computed: the radial-difference (RD) and the angular-difference (AD). Inspired by the LBP approach, two intensity-based descriptors CI-LBP and NI-LBP, and two difference-based descriptors RD-LBP and AD-LBP are developed. All four descriptors are in the same form as conventional LBP codes, so they can be readily combined to form joint histograms to represent textured images. The proposed approach is computationally very simple: it is totally training-free, there is no need to learn a texton dictionary, and no tuning of parameters. We have conducted extensive experiments on three challenging texture databases (Outex, CUReT and KTHTIPS2b). Outex results show significant improvements over the classical LBP approach, which clearly demonstrates the great power of the joint distributions of these proposed descriptors for gray-scale and rotation invariant texture classification. The proposed method produces the best classification results on KTHTIPS2b, and results comparable to the state-of-the-art on CUReT. Local feature descriptor is an important module for face recognition and those like Gabor and local binary patterns (LBP) have proven effective face descriptors. Traditionally, the form of such local descriptors is predefined in a handcrafted way. In this paper, we propose a method to learn a discriminant face descriptor (DFD) in a data-driven way. The idea is to learn the most discriminant local features that minimize the difference of the features between images of the same person and maximize that between images from different people. In particular, we propose to enhance the discriminative ability of face representation in three aspects. First, the discriminant image filters are learned. Second, the optimal neighborhood sampling strategy is soft determined. Third, the dominant patterns are statistically constructed. Discriminative learning is incorporated to extract effective and robust features. We further apply the proposed method to the heterogeneous (cross-modality) face recognition problem and learn DFD in a coupled way (coupled DFD or C-DFD) to reduce the gap between features of heterogeneous face images to improve the performance of this challenging problem. Extensive experiments on FERET, CAS-PEAL-R1, LFW, and HFB face databases validate the effectiveness of the proposed DFD learning on both homogeneous and heterogeneous face recognition problems. The DFD improves POEM and LQP by about 4.5 percent on LFW database and the C-DFD enhances the heterogeneous face recognition performance of LBP by over 25 percent. Motivated by the discriminative ability of shape information and local patterns in object recognition, this paper proposes a window-based object descriptor that integrates both cues. In particular, contour templates representing object shape are used to derive a set of so-called key points at which local appearance features are extracted. These key points are located using an improved template matching method that utilises both spatial and orientation information in a simple and effective way. At each of the extracted key points, a new local appearance feature, namely non-redundant local binary pattern (NR-LBP), is computed. An object descriptor is formed by concatenating the NR-LBP features from all key points to encode the shape as well as the appearance of the object. The proposed descriptor was extensively tested in the task of detecting humans from static images on the commonly used MIT and INRIA datasets. The experimental results have shown that the proposed descriptor can effectively describe non-rigid objects with high articulation and improve the detection rate compared to other state-of-the-art object descriptors.