Signature recognition

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

Biometric Recognition Performing in a Bioinspired System

Abstract: In this study, we propose a set of biometric recognition experiments in similar conditions to real operating systems. This implies a jump from the usual laboratory conditions to a more real situation where the amount of variability between training and testing samples is large. We present experiments with face, hand-geometry, and signature recognition training a “universal classifier” able to decide if two input samples belong to the same person or not. During test, we recognize samples of a different database not used during classifier training. Training with the ORL face database and testing with the AR database provides a 5.1% error rate in verification operation, while training and testing with the same database yields 2.5%. For hand-geometry databases, we obtain 4.33 and 0.16% for different and same testing and training databases, respectively. For signature recognition, we obtain 1.36 and 4.14% for different and same testing and training databases, respectively. Our proposed system implies a very low computational cost to introduce/remove a user in the database, which is a crucial point for a real operation biometric system.

Face Recognition using SIFT Descriptor under Multiple Paradigms of Graph Similarity Constraints 1

Abstract: Biometric systems are considered as human pattern recognition systems that can be used for individual identification and verification. The decision on the authenticity is done with the help of some specific measurable physiological or behavioral characteristics possessed by the individuals. Robust architecture of any biometric system provides very good performance of the system against rotation, translation, scaling effect and deformation of the image on the image plane. Further, there is a need of development of real-time biometric system. There exist many graph matching techniques used to design robust and real-time biometrics systems. This paper discusses two graph matching techniques that have been successfully used in face biometric traits.

Using Hidden Markov Models for Biometric Gait Recognition

Abstract: The need for secure authentication to mobile devices is rapidly increasing with the advent of new technologies. Many of the new mobile devices can be used for various purposes such as internet access, mobile banking, calender etc. As a result of this, sensitive information like phone numbers, address contacts and even financial information are stored on these devices. When valuable information like this is present, it raises serious concerns in case the device is lost or stolen. For protection of the device contents, this thesis proposes a biometric gait recognition method based on the accelerometer data obtained from the mobile device. This method offers an unobtrusive and hence user friendly way for authentication on mobile devices. Biometric gait recognition based on accelerometer data is still a new field of research. Most of the existing methods use dedicated accelerometers to collect gait data and then use a suitable cycle extraction method to extract the gait features. As cycle extraction methods are sensitive towards irregular cycles, this often affects the error rates of such systems. In this thesis, Hidden Markov Models are used for biometric gait recognition. These have already been successfully implemented on various commercial speaker recognition systems, but have never been used for biometric gait recognition. The advantage of this method is that no error-prone cycle extraction has to be performed, but the accelerometer data can be directly used to construct the model and thus form the basis for successful recognition. Two major experiments were conducted: using different preprocessed data sets and using different model topologies, for training and testing the Hidden Markov models. By using the accelerometer data obtained from mobile phones, a false rejection rate (FRR) of 10.42% at a false acceptance rate (FAR) of 9.31% was obtained.

Image processing apparatus, image processing method, signature registration program, and storage medium

Abstract: Signature data of a handwritten signature input to a signature authentication device is analyzed to determine whether to register the signature based on the characteristics of the stroke shape of the signature. If the signature registration is denied, a response message for making the signature registerable is displayed according to the way in which the signature is written. At least one of cumulative angle changes in the locus of the signature, fluctuation in the speed at which the signature was written, fluctuation in the size of the characters included in the signature, and fluctuation in the center positions of the characters included in the signature is used as the determination criterion.