Signature recognition

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

Canonical Correlation Analysis on Fusion of Global and Local Features of Face Recognition

Abstract: Face recognition belongs to the important content of the biometric identification, which is a important method in research of image processing and pattern recognition. It can effectively overcome the traditional authentication defects Through the facial recognition technology. At present, face recognition under ideal state research made some achievements, but the changes in light, shade, expression, posture changes the interference factors such as face recognition is still exist many problems. For this, put forward the integration of global and local features of face recognition research. Practice has proved that through the effective integration of global features and local characteristics, build based on global features and local features fusion face recognition system, can improve the recognition rate of face recognition, face recognition application benefit.

Connected-digits recognition for an under-resourced language using Hidden Markov Models

Abstract: This paper presents the development of a speech recognition system for automatically recognizing fluently spoken digit strings in Northern Sotho. The digit strings can be isolated or connected/continuous with known or unknown length. The digit recognition system has been trained with the aim of satisfying its potential end-users. Our main research focus was to enhance the robustness of a connected-digits recognizer such that it can handle continuous speech input restricted to numeric digits vocabularies. The Hidden Markov Model Toolkit (HTK) was used for experimentation. The standard technique that is based on the use of hidden Markov models (HMMs) was augmented with Cepstral Mean Vector Normalization (CMVN); a technique designed to handle convoluted distortions with the aim of increasing the robustness of speech recognition systems. A 1255 words dataset extracted from an existing general-purpose Northern Sotho speech database collected from mother tongue speakers between the ages of 16 and 60 was used in our experiment. The CMVN technique obtained a phone recognition accuracy of 75.84% and a word recognition accuracy of 62.30% whereas the standard HMM-based technique obtained phone recognition accuracy of 72.45% and a word recognition accuracy of 4.57%.

Spatial frequency domain image processing for biometric recognition

Abstract: Biometric recognition refers to the process of matching an input biometric to stored biometric information. In particular, biometric verification refers to matching the live biometric input from an individual to the stored biometric template about that individual. Examples of biometrics include face images, fingerprint images, iris images. retinal scans, etc. Thus, image processing techniques prove useful in the biometric recognition. In this paper, we discuss spatial frequency domain image processing methods useful for biometric recognition.

10MHz 64 Bit Error Tolerant Signature Recognition Circuit

Abstract: This paper a circuit will be presented which identifies a 64 bit synchronization word in a serial incoming data stream. The signature is identified when the incoming data stream matches a previously known and stored signature. It is also possible to program the number of bit errors permissible for recognition of the signature. The circuit was fabricated in a 1.5?m CMOS technology for use in a D2MAC television decoder. The circuit operates at data rates up to 20MHz and covers an area of 1.4 × 0.1 mm2

Multiple fault diagnosis by signature recognition of time-varying residuals

Abstract: A Fault Detection and Diagnosis scheme able to deal with concurrent, incipient, sensor and actuator faults is presented. The architecture allows the diagnosis whenever the system's outputs are less than the number of faults. Residual generation is achieved by taking advantage from observer-based design, whereas the classification is performed by extending the concept of directional residual towards a time-varying setting. The scheme is designed to leverage the power from both model-based and data-driven approaches while mitigating their inherent drawbacks. The performances of the proposed strategy are evaluated by employing real data coming from the TEKOB1 Thermal Plant of Kostolac, Serbia.