Signature recognition

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

Phone-dependent channel compensated hidden Markov model for telephone speech recognition

Abstract: We propose the phone-dependent channel compensated hidden Markov model (PDCC-HMM) for telephone speech recognition. The PDCC-HMM is derived by modifying the conventional hidden Markov model (HMM) with the phone-dependent channel compensation vectors. The telephone speech is recognized efficiently by using the derived PDCC-HMM. Experiments demonstrate the robustness of PDCC-HMM in speech recognition and show the significant reduction of recognition error rate by 50% compared to the conventional HMM method.

Ship Noise Evaluation Based On Segmented Decision Trees

Abstract: Signature recognition can be useful in a wide range of applications. A decision tree method for ship noise classification is presented. Thie ship noise, once transposed to the frequency's domain through the application of a b"Ii'r, is classified by a decision tree previously calculated during a training phase in which a significant set of situations must be shown to the algorithm. The tree calculation process is explained and results from real e xperiments a re presented. P arallel implementations for improvement of performance are suggested. A well suited architecture transputer based, is also presented for the problem solution.

Enhanced Face Recognition System Combining PCA, LDA, ICA with Wavelet Packets and Curvelets

Abstract: Face recognition is one of the most frequently used biometrics both in commercial and law enforcement applications. The individuality of facial recognition from other biometric techniques is that it can be used for surveillance purposes; as in searching for wanted criminals, suspected terrorists, and missing children. The steps in a face recognition steps are preprocessing (image enhancement), feature extraction and finally recognition. This paper identifies techniques in each step of the recognition process to improve the overall performance of face recognition. The proposed face recognition model combines enhanced 2DPCA algorithm, LDA, ICA with wavelet packets and curvelets and experimental results proves that the combination of these techniques increases the efficiency of the recognition process and improves the existing systems.

Signature Verification Using Artificial Neural Network

Abstract: The human signature is most important for access. Signature of the person is important biometric attribute of a human being which is used to authenticate human identity. There are many biometric characteristics by which one can have own identity like face recognition, fingerprint detection, iris inspection and retina scanning. In non-vision based techniques voice recognition and signature verification are most widely used. Verification can be performed either Online or Offline. Online system of signature verification uses dynamic information of a signature captured at the time the signature is made. Offline system uses scanned image of signature. In this paper, I present a method for Offline Verification of signatures using a set of simple shape based geometric features. As signatures play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. This paper presents the off-line signature recognition & verification using neural network in which the human signature is captured and presented in the image format. Various image processing techniques are used to recognize and verify the signature. Preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. Initially system use database of signatures obtained from those individuals whose signatures have to be authenticated by the system. Then artificial neural network (ANN) is used to verify and classify the signatures. The implementation details and results are discussed in the paper.

A study on Chinese carbon-signature recognition

Abstract: A novel off-line algorithm and a modified chain code for recognizing signatures are proposed in the present study. Carbon paper is used to detect force distributions when people write their signatures. First of all, the signature contours are located and the upper and lower profiles are generated; then, these are used to classify the given signature. Both the gray-scale and the chain code characteristics of a signature are used to extract structure and force distribution features, which are then transformed into a normalized vector. Finally, a Supervised Fuzzy Adaptive Hamming Network (SFAHN) is employed to interpret the feature vector in order to determine whether the signature is genuine or not. Simulation results show that the proposed algorithm has a good recognition rate.