Alan McCabe

Bio: Alan McCabe is an academic researcher from James Cook University. The author has contributed to research in topics: Signature (logic) & Authentication. The author has an hindex of 8, co-authored 14 publications receiving 266 citations. Previous affiliations of Alan McCabe include Maharaja Agrasen Institute of Technology.

Neural Network-based Handwritten Signature Verification

Abstract: Handwritten signatures are considered as the most natural method of authenticating a person’s identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using a NN architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. Several Network topologies are tested and their accuracy is compared. The resulting system performs reasonably well with an overall error rate of 3:3% being reported for the best case.

Artificial Intelligence in Sports Prediction

Abstract: This paper presents an extension of earlier work in the use of artificial intelligence for prediction of sporting outcomes. An expanded model is described, as well as a broadening of the area of application of the original work. The model used is a form of multi-layer perceptron and it is presented with a number of features which attempt to capture the quality of various sporting teams. The system performs well and compares favourably with human tipsters in several environments. A study of less rigid "World Cup" formats appears, along with extensive live testing results in a major international tipping competition.

Neural Network-based Hwritten Signature Verification.

Abstract: Handwritten signatures are considered as the most natural method of authenticating a person’s identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using a NN architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. Several Network topologies are tested and their accuracy is compared. The resulting system performs reasonably well with an overall error rate of 3:3% being reported for the best case.

Remote handwritten signature authentication

Abstract: This paper presents a secure real time remote user authentication system based on dynamic handwritten signature verification. The system allows users to establish their identities to other parties in real-time via a trusted verification server. The system can be used to gain remote access to restricted content on a server or to verify a signature on a legal document. State of the art dynamic verification techniques are combined with proven cryptographic methods to develop a secure model for remote handwritten signature verification.

Handwritten signature verification using complementary statistical models

Abstract: This paper describes a system for performing handwritten signature verification using complementary statistical models. The system analyses both the static features of a signature (e.g., shape, slant, size), and its dynamic features (e.g., velocity, pen-tip pressure, timing) to form a judgment about the signer’s identity. This approach’s novelty lies in combining output from existing Neural Network and Hidden Markov Model based signature verification systems to improve the robustness of any specific approach used alone. The system performs reasonably well and achieves an overall error rate of 2:1% in the best case. The results of several other experiments are also presented including using less reference signatures, allowing multiple signing attempts, zero- effort forgery attempts, providing visual feedback, and signing a password rather than a signature.

[서평]「Applied Cryptography」

Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

Context-Sensitive Learning for Enhanced Audiovisual Emotion Classification

Abstract: Human emotional expression tends to evolve in a structured manner in the sense that certain emotional evolution patterns, i.e., anger to anger, are more probable than others, e.g., anger to happiness. Furthermore, the perception of an emotional display can be affected by recent emotional displays. Therefore, the emotional content of past and future observations could offer relevant temporal context when classifying the emotional content of an observation. In this work, we focus on audio-visual recognition of the emotional content of improvised emotional interactions at the utterance level. We examine context-sensitive schemes for emotion recognition within a multimodal, hierarchical approach: bidirectional Long Short-Term Memory (BLSTM) neural networks, hierarchical Hidden Markov Model classifiers (HMMs), and hybrid HMM/BLSTM classifiers are considered for modeling emotion evolution within an utterance and between utterances over the course of a dialog. Overall, our experimental results indicate that incorporating long-term temporal context is beneficial for emotion recognition systems that encounter a variety of emotional manifestations. Context-sensitive approaches outperform those without context for classification tasks such as discrimination between valence levels or between clusters in the valence-activation space. The analysis of emotional transitions in our database sheds light into the flow of affective expressions, revealing potentially useful patterns.

Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions

Abstract: In this paper, a new technique for online signature verification or identification is proposed. The technique integrates a longest common subsequences (LCSS) detection algorithm which measures the similarity of signature time series into a kernel function for support vector machines (SVM). LCSS offers the possibility to consider the local variability of signals such as the time series of pen-tip coordinates on a graphic tablet, forces on a pen, or inclination angles of a pen measured during a signing process. Consequently, the similarity of two signature time series can be determined in a more reliable way than with other measures. A proprietary database with signatures of 153 test persons and the SVC 2004 benchmark database are used to show the properties of the new SVM-LCSS. We investigate its parameterization and compare it to SVM with other kernel functions such as dynamic time warping (DTW). Our experiments show that SVM with the LCSS kernel authenticate persons very reliably and with a performance which is significantly better than that of the best comparing technique, SVM with DTW kernel.