Signature recognition

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

Biometric image protection based on discrete cosine transform watermarking technique

Abstract: This paper presents combined face recognition and watermarking technique to secure a biometric image and maintain the recognition rate. These days, with advanced technology, the biometric data can be stolen and faked which may be used in other applications that utilize the same biometric feature. The discrete cosine transform (DCT) watermarking technique is used to certify the biometric image belong to the legitimate user. We have tested the proposed scheme under several attacks. The results of the experimentations show that the face recognition rate performance almost does not degrade due to watermark embedding and the proposed scheme is robust against several attacks.

A classification of biometric signatures

Abstract: The domain of biometrics lacks of a systematical approach for classifying biometric signatures for biometric authentication, detection, and reaction systems. This paper presents a first approach to fill this gap. Outlining the general authentication process and analyzing the meaning of the term signature from selected sciences, a definition of the term biometric signature as (bin|n-)ary coded representation of biometric characteristics is derived. To show the suitability of the suggested definition, its role within the core processes of biometric authentication systems (enrollment, authentication, derollment) is described.

Method and System for Multi-Modal Identity Recognition

Abstract: A device, a system and a method are provided for multi-modal identity recognition. The device includes a face recognition unit, a voice recognition unit, and a control unit. The face recognition unit is configured for generating a first recognition result by obtaining and processing face recognition information of a customer and by comparing the processed face recognition information with face recognition information stored in a facial feature database. The voice recognition unit is configured for generating a second recognition result by obtaining and processing voice recognition information of a customer and by comparing the processed voice recognition information with voice recognition information stored in an audio signature database. The control unit is configured for confirming an identity of the customer based on the first recognition result and the second recognition result.

Shape-Based Human Activity Recognition Using Independent Component Analysis and Hidden Markov Model

Abstract: In this paper, a novel human activity recognition method is proposed which utilizes independent components of activity shape information from image sequences and Hidden Markov Model (HMM) for recognition. Activities are represented by feature vectors from Independent Component Analysis (ICA) on video images and based on these features, recognition is achieved by trained HMMs of activities. Our recognition performance has been compared to the conventional method where Principle Component Analysis (PCA) is typically used to derive activity shape features. Our results show that superior recognition is achieved with our proposed method especially for activities (e.g., skipping) that cannot be easily recognized by the conventional method.