Collaborating Authors

A Directional Feature with Energy based Offline Signature Verification Network Artificial Intelligence

Signature used as a biometric is implemented in various systems as well as every signature signed by each person is distinct at the same time. So, it is very important to have a computerized signature verification system. In offline signature verification system dynamic features are not available obviously, but one can use a signature as an image and apply image processing techniques to make an effective offline signature verification system. Author proposes a intelligent network used directional feature and energy density both as inputs to the same network and classifies the signature. Neural network is used as a classifier for this system. The results are compared with both the very basic energy density method and a simple directional feature method of offline signature verification system and this proposed new network is found very effective as compared to the above two methods, specially for less number of training samples, which can be implemented practically.

Offline Handwritten Signature Verification - Literature Review Machine Learning

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.

'Creative' Facial Verification with Generative Adversarial Networks


A new paper from Stanford University has proposed a nascent method for fooling facial authentication systems in platforms such as dating apps, by using a Generative Adversarial Network (GAN) to create alternative face images that contain the same essential ID information as a real face. The method successfully bypassed facial verification processes on dating applications Tinder and Bumble, in one case even passing off a gender-swapped (male) face as authentic to the source (female) identity. Various generated identities which feature the specific encoding of the paper's author (featured in first image above). According to the author, the work represents the first attempt to bypass facial verification with the use of generated images that have been imbued with specific identity traits, but which attempt to represent an alternate or substantially altered identity. The technique was tested on a custom local face verification system, and then performed well in black box tests against two dating applications that perform facial verification on user-uploaded images.

Writer Independent Offline Signature Recognition Using Ensemble Learning Machine Learning

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. 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 verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we have proposed an Ensemble model for offline writer, independent signature verification task with Deep learning. We have used two CNNs for feature extraction, after that RGBT for classification & Stacking to generate final prediction vector. We have done extensive experiments on various datasets from various sources to maintain a variance in the dataset. We have achieved the state of the art performance on various datasets.

Signature Verification using Geometrical Features and Artificial Neural Network Classifier Artificial Intelligence

Signature verification has been one of the major researched areas in the field of computer vision. Many financial and legal organizations use signature verification as access control and authentication. Signature images are not rich in texture; however, they have much vital geometrical information. Through this work, we have proposed a signature verification methodology that is simple yet effective. The technique presented in this paper harnesses the geometrical features of a signature image like center, isolated points, connected components, etc., and with the power of Artificial Neural Network (ANN) classifier, classifies the signature image based on their geometrical features. Publicly available dataset MCYT, BHSig260 (contains the image of two regional languages Bengali and Hindi) has been used in this paper to test the effectiveness of the proposed method. We have received a lower Equal Error Rate (EER) on MCYT 100 dataset and higher accuracy on the BHSig260 dataset.