Signature recognition

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

Effects of watermarking on iris recognition performance

Abstract: Protection of biometric data and templates is a crucial issue for the security of biometric systems, and biometric watermarking is introduced for this purpose. However, watermarking introduces extra information into the biometric data (biometric images or biometric feature templates) which leads to certain distortion. In addition, watermarked images are always subject to the risk of being attacked. Hence, whether and how biometric recognition performance will be affected by biometric watermarking deserves investigation. In this paper, we make a first attempt in such investigations by studying two application scenarios in the context of iris recognition, namely protection of iris templates by hiding them in cover images as watermarks (iris watermarks), and protection of iris images by watermarking them. Experimental results suggest that watermark embedding in iris images does not introduce detectable decreases on iris recognition performance whereas recognition performance drops significantly if iris watermarks suffer from severe attacks.

Recognition of head gestures using hidden Markov models

Abstract: This paper explores the use of hidden Markov models (HMMs) for the recognition of head gestures. A gesture corresponds to a particular pattern of head movement. The facial plane is tracked using a parameterized model and the temporal sequence of three image rotation parameters are used to describe four gestures. A dynamic vector quantization scheme was implemented to transform the parameters into suitable input data for the HMMs. Each model was trained by the iterative Baum-Welch procedure using 28 sequences taken from 5 persons. Experimental results from a different data set (33 new sequences from 6 other persons) demonstrate the effectiveness of this approach.

System and method for capturing and applying a legal signature to documents over a network

Abstract: An electronic signature capture system and process meets the legal requirements of a valid electronic signature while also providing electronically signed documents that have the appearance, and thus equivalent acceptability, of a traditional pen-and-ink signature. The documents can be signed using a mouse, a stylus, a touch screen, a graphics tablet, or other suitable input device to draw a signature analogue on the screen similar to signing a paper document with a pen. A fingerprint image, retinal scan image, or other similar biometric input may be captured in addition to or instead of a signature. The signature analogue is saved and linked to a particular user and to particular documents. The signature analogue may be combined with the document in a composite image file, or the signature analogue may be applied dynamically to appropriate document components to assemble an executed document as needed.

Embedded System for Biometric Online Signature Verification

Abstract: This paper describes the implementation on field-programmable gate arrays (FPGAs) of an embedded system for online signature verification. The recognition algorithm mainly consists of three stages. First, an initial preprocessing is applied on the captured signature, removing noise and normalizing information related to horizontal and vertical positions. Afterwards, a dynamic time warping algorithm is used to align this processed signature with its template previously stored in a database. Finally, a set of features are extracted and passed through a Gaussian Mixture Model, which reveals the degree of similarity between both signatures. The algorithm was tested using a public database of 100 users, obtaining high recognition rates for both genuine and forgery signatures. The implemented system consists of a vector floating-point unit (VFPU), specifically designed for accelerating the floating-point computations involved in this biometric modality. Moreover, the proposed architecture also includes a microprocessor, which interacts with the VFPU, and executes by software the rest of the online signature verification process. The designed system is capable of finishing a complete verification in less than 68 ms with a clock rated at 40 MHz. Experimental results show that the number of clock cycles is accelerated by a factor of ×4.8 and ×11.1, when compared with systems based on ARM Cortex-A8 and when substituting the VFPU by the Floating-Point Unit provided by Xilinx, respectively.