Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Continuous Gesture Recognition Based on Hidden Markov Model

Abstract: Gesture is a compelling interactive mode, which makes interaction become more active than before. With the development of acceleration sensor, it has played an important role in gesture recognition of human-computer interaction. This paper represents a gesture recognition based on accelerometer, which is modeled by Hidden Markov Model (HMM). For “continuous” gesture recognition, it is a vital problem of how to obtain real valid data in a series of raw gesture data accurately and efficiently. To solve this, we proposed a new gesture detection method based on energy entropy and combined with threshold. Gesture data is analyzed in energy distribution of frequency domain by Short Time Fourier Transform (STFT), which can calculate energy entropy that reflects signal energy distribution. Then an appropriate threshold is set up to determine the start and end of gesture. Through experiments, the proposed method can be proved that it works well in detecting valid gesture data while recognition time and the computation load can be reduced in the case of guaranteeing recognition precision.

An on-line Chinese signature verification system using an impulse response of signature generation model

Abstract: Chinese signature verification is the leading edge in the field of Chinese information processing. This paper presents a function method for signature verification using an impulse response of signature generation model, which is more strict than the parameter method that is used widely. In order to avoid high order inverse matrix calculating, an approach of segmentation proceed with the administrative levels of Chinese character frames, and stroke-based segmentation is used. So that an impulse response of signature generation model can be used for signature verification. The experiments reveal the feasibility of the method.

Biometric Recognition Systems-Multimodal over Unimodel

Abstract: Biometric recognition system gained superb popularity because of its uniqueness and wide applications. Uniqueness lies in the factor that each and every human being"s biometrics features are unique and inevitable. Most of the present researcher deals with improvement of these biometric systems, in a way that either the feature extractions from the image become good or matching of the feature becomes exact. Single modality biometric recognition systems are usually result degraded performance because of erroneous/modified pattern, for example finger print recognition system delivers problem due to scratched, wet, colored or tattooed fingers. In the similar manner 2D images/patterns of face recognition system can dramatically change due to lighting and viewing variations. Hence, in the recent past years, scientist/researchers combined biometric recognition system to improve the recognition performance.

Matching and Retrieving Sequential Patterns Under Regression

Abstract: Sequential pattern matching and retrieving is of real value. For example, finding stocks in the NASDAQ market whose closing prices are always about $β₀ higher than or β₁ times as that of a given company. The probelm reduces to linear pattern retrieval: given query X, find all sequence Y from database S so that Y = β₀ + β₁ with confidence C. In this paper, we novelly introduce SLR (Simple Linear Regression) model [5,7] to solve this problem. We extend 1-dimensional R^2 to ER^2 for multi-dimensional sequence matching, such as on-line handwritten signature. In addition, we develop SLR+FFT pruning techniques based on SLR to speed up retrieval without incurring any false dismissal. Experimental results show that the pruning ratio of SLR+FFT is efficient (can be above 99%). Experiments on real stocks discovered many interesting patterns. Preliminary test on on-line signature recognition using ER^2 as similarity measure also shows high accuracy.