Topic
Signature recognition
About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.
Papers published on a yearly basis
Papers
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TL;DR: A new method based on amplitude modulation is presented that has shown to be resistant to both classical attacks, such as filtering, and geometrical attacks and can be extracted without the original image.
Abstract: Watermarking techniques, also referred to as digital signature, sign images by introducing changes that are imperceptible to the human eye but easily recoverable by a computer program. Generally, the signature is a number which identifies the owner of the image. The locations in the image where the signature is embedded are determined by a secret key. Doing so prevents possible pirates from easily removing the signature. Furthermore, it should be possible to retrieve the signature from an altered image. Possible alternations of signed images include blurring, compression and geometrical transformations such as rotation and translation. These alterations are referred to as attacks. A new method based on amplitude modulation is presented. Single signature bits are multiply embedded by modifying pixel values in the blue channel. These modifications are either additive or subtractive, depending on the value of the bit, and proportional to the luminance. This new method has shown to be resistant to both classical attacks, such as filtering, and geometrical attacks. Moreover, the signature can be extracted without the original image.
408 citations
TL;DR: This work presents a system for online handwritten signature verification, approaching the problem as a two-class pattern recognition problem, and received the first place at SVC2004 with a 2.8% error rate.
399 citations
Patent•
10 Sep 1999
TL;DR: A method and apparatus for biometric recognition of an individual living organism using electric and/or magnetic or acoustic energy was described in this paper. But this method was not suitable for the detection of a single living organism.
Abstract: A method and apparatus for biometric recognition (10) of an individual living organism using electric and/or magnetic or acoustic energy. A biometric recognition system for measuring at least one bio-electrical and/or biomagnetic property. A method and apparatus for recognition of an individual living organism's identity. A method and apparatus for identifying electric and/or magnetic properties of an individual living organism. A method and apparatus for diagnosing a bone. A method and apparatus for sensing an induced current (12) in an individual living organism. A method for using a computer (22). A method for secure communication between an individual at a first location and a second location. A method and apparatus for sensing the electric and/or magnetic properties of an individual living organism using acoustic energy.
381 citations
26 Apr 1985
TL;DR: The combination of general spectral information and specific acoustic-phonetic features is shown to result in more accurate phonetic recognition than either representation by itself.
Abstract: This paper describes the results of our work in designing a system for phonetic recognition of unrestricted continuous speech. We describe several algorithms used to recognize phonemes using context-dependent Hidden Markov Models of the phonemes. We present results for several variations of the parameters of the algorithms. In addition, we propose a technique that makes it possible to integrate traditional acoustic-phonetic features into a hidden Markov process. The categorical decisions usually associated with heuristic acoustic-phonetic algorithms are replaced by automated training techniques and global search strategies. The combination of general spectral information and specific acoustic-phonetic features is shown to result in more accurate phonetic recognition than either representation by itself.
367 citations
Book•
24 Nov 2005TL;DR: This 2005 book provides a needed review of signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises and shows both digital and optical implementations.
Abstract: Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition The design, analysis and use of correlation pattern recognition algorithms requires background information, including linear systems theory, random variables and processes, matrix/vector methods, detection and estimation theory, digital signal processing and optical processing This 2005 book provides a needed review of this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises It shows both digital and optical implementations It also contains technology presented by the team that developed it and includes case studies of significant interest, such as face and fingerprint recognition Suitable for graduate students taking courses in pattern recognition theory, whilst reaching technical levels of interest to the professional practitioner
366 citations