Muhammad Imran Malik

Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)

The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic casework. In collaboration with the German Research Center for Artificial Intelligence we have organized a signature verification competition on datasets with two scripts (Dutch and Chinese) in which we asked to compare questioned signatures against a set of reference signatures. We have received 12 systems from 5 institutes and performed experiments on online and offline Dutch and Chinese signatures. For evaluation, we applied methods used by Forensic Handwriting Examiners (FHEs) to assess the value of the evidence, i.e., we took the likelihood ratios more into account than in previous competitions. The data set was quite challenging and the results are very interesting.

ICDAR 2013 Competitions on Signature Verification and Writer Identification for On- and Offline Skilled Forgeries (SigWiComp 2013)

This paper presents the results of the ICDAR2013 competitions on signature verification and writer identification for on- and offline skilled forgeries jointly organized by PR researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. Two modalities (signatures, and handwritten text) are considered where training and evaluation data (in Dutch and Japanese) were collected and provided by FHEs and PR-researchers. Four tasks were defined where the systems had to perform Dutch offline signature verification, Japanese offline signature verification, Japanese online signature verification, and Dutch writer identification. The participants of the signatures modality were motivated to report their results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic casework. For evaluation of signatures modality, we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Ĉllr). The system having the smallest value of the Minimum Cost of Log Likelihood Ratio (Ĉllrmin) is declared winner. For evaluation of the handwritten text modality, we used the precision and accuracy measures and winners are announced on the basis of best F-measure value.

Forensic Signature Verification Competition 4NSigComp2010 - Detection of Simulated and Disguised Signatures

This competition scenario aims at a performance comparison of several automated systems for the task of signature verification. The systems have to rate the probability of authorship and non-authorship of signatures. In particular they have to determine whether questioned signatures are simulated disguised or the normal signature of the reference writer. Furthermore, the results will be compared to forensic handwriting examiners (FHEs) opinions on the same tasks. As such, to the best of the authors’ knowledge, this scenario will be the first attempt in literature to relate system performances to the performance of FHEs who gave their opinion on exactly the the same signatures.

Automatic Signature Stability Analysis and Verification Using Local Features

The purpose of writing this paper is two-fold. First, it presents a novel signature stability analysis based on signatures local / part-based features. The Speeded Up Local features (SURF) are used for local analysis which give various clues about the potential areas from whom the features should be exclusively considered while performing signature verification. Second, based on the results of the local stability analysis we present a novel signature verification system and evaluate this system on the publicly available dataset of forensic signature verification competition, 4NSigComp2010, which contains genuine, forged, and disguised signatures. The proposed system achieved an EER of 15%, which is considerably very low when compared against all the participants of the said competition. Furthermore, we also compare the proposed system with some of the earlier reported systems on the said data. The proposed system also outperforms these systems.

ICFHR 2012 Competition on Automatic Forensic Signature Verification (4NsigComp 2012)

This paper presents the results of the ICFHR2012 Competition on Automatic Forensic Signature Verification jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. A forensic like training set containing disguised signatures along with skilled forgeries and genuine signatures was provided to the participants. They were motivated to report the results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic casework. For evaluation we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Ĉllr). The system having the best Minimum Cost of Log Likelihood Ratio ( Ĉllrmin) is declared winner. Various experiments both including and excluding disguised signatures from the test set are reported.

A Signature Verification Framework for Digital Pen Applications

In this paper we present a framework for real-time online signature verification scenarios. The proposed framework is based on state-of-the-art feature extraction and Gaussian Mixture Model (GMM) classification. While our signature verification library is generally applicable to any input device using digital pens, we have implemented verification scenarios using the Anoto digital pen. As such our automated signature verification framework becomes an interesting commodity for industry, because the Anoto SDK is easy to apply and the GMM-based classification can be seamlessly integrated. The novelty of this work is the application of our framework that takes real-time online signature verification to every scenario where digital pens may potentially be used. In this paper we describe several scenarios where our framework has been applied, including signatures in financial contracts or ordering processes. We also propose a general approach to integrate the GMM-descriptions into electronic ID-cards in order to also store behavioral biometrics on these cards. In experiments we have measured the performance of the signature verification system when skilled forgeries were present. The interest shown by our partner financial institutions and the results of our initial evaluations indicate that our signature verification framework suits exactly the demands of our clients.

Signature Segmentation from Document Images

In this paper we propose a novel method for the extraction of signatures from document images. Instead of using a human defined set of features a part-based feature extraction method is used. In particular, we use the Speeded Up Robust Features (SURF) to distinguish the machine printed text from signatures. Using SURF features makes the approach generally more useful and reliable for different resolution documents. We have evaluated our system on the publicly available Tobacco-800 dataset in order to compare it to previous work. Finally, all signatures were found in the images and less than half of the found signatures are false positives. Therefore, our system can be applied for practical use.

From Terminology to Evaluation: Performance Assessment of Automatic Signature Verification Systems

This paper is an effort towards the development of a shared conceptualization regarding automatic signature verification systems. The requirements of both communities, Pattern Recognition and Forensic Handwriting Examiners, are explicitly focused. This is required because an increasing gap regarding evaluation of automatic verification systems is observed in the recent past. The paper addresses three major areas. First, it highlights how signature verification is taken differently in the above mentioned communities and why this gap is increasing. Various factors that widen this gap are discussed with reference to some of the recent signature verification studies and probable solutions are suggested. Second, it discusses the state-of-the-art evaluation and its problems as seen by FHEs. The real evaluation issues faced by FHEs, when trying to incorporate automatic signature verification systems in their routine casework, are presented. Third, it reports a standardized evaluation scheme capable of fulfilling the requirements of both PR researchers and FHEs.

Automatic fish species classification in underwater videos: Exploiting pre-trained deep neural network models to compensate for limited labelled data

There is a need for automatic systems that can reliably detect, track and classify fish and other marine species in underwater videos without human intervention. Conventional computer vision techniques do not perform well in underwater conditions where the background is complex and the shape and textural features of fish are subtle. Data-driven classification models like neural networks require a huge amount of labelled data, otherwise they tend to over-fit to the training data and fail on unseen test data which is not involved in training. We present a state-of-the-art computer vision method for fine-grained fish species classification based on deep learning techniques. A cross-layer pooling algorithm using a pre-trained Convolutional Neural Network as a generalized feature detector is proposed, thus avoiding the need for a large amount of training data. Classification on test data is performed by a SVM on the features computed through the proposed method, resulting in classification accuracy of 94.3% for fish species from typical underwater video imagery captured off the coast of Western Australia. This research advocates that the development of automated classification systems which can identify fish from underwater video imagery is feasible and a cost-effective alternative to manual identification by humans.

FREAK for Real Time Forensic Signature Verification

This paper presents a novel signature verification system based on local features of signatures. The proposed system uses Fast Retina Key points (FREAK) which represent local features and are inspired by the human visual system, particularly the retina. To locate local points of interest in signatures, two local key point detectors, i.e., Features from Accelerated Segment Test (FAST) and Speeded-up Robust Features (SURF), have been used and their performance comparison in terms of Equal Error Rate (EER) and time is presented. The proposed system has been evaluated on publicly available dataset of forensic signature verification competition, 4NSigComp2010, which contains genuine, forged, and disguised signatures. The proposed system achieved an EER of 30%, which is considerably very low when compared against all the participants of the said competition. In addition to EER, the proposed system requires only 0.6 seconds on average to verify a 3000*1500 scanned signature. This shows that the proposed system has a potential and suitability for forensic signature verification as well as real time applications.