Showing papers on "Signature recognition published in 1987"

Signature recognition through spectral analysis

Abstract: Features such as shape, motion and pressure, minutiae details and timing, and transformation methods such as Hadamard and Walsh have been used in signature recognition with various degrees of success. One of the better studies was done by Sato and Kogure using nonlinear warping function. However, it is time consuming in terms of computer time and programming time. In this research, the signatures were normalized for size, orientation, etc. After normalization, the X and Y coordinates of each sampled point of a signature over time (to capture the dynamics of signature writing) were represented as a complex number and the set of complex numbers transformed into the frequency domain via the fast Fourier transform. A Gaussian probabilistic model was developed to screen and select from the large set of features (e.g. amplitude of each harmonics). The significant harmonics of the signature were sorted according to the chi-square value, which is equivalent to the signal-to-noise ratio. Fifteen harmonics with the largest signal-to-noise ratios from the true signatures were used in a discriminant analysis. A total of eight true signatures from a single person and eight each from nineteen forgers were used. It results in an error rate of 2.5%, with the normally more conservative jacknife procedure yielding the same small error rate.

Speech recognition using semi-hidden Markov models of multiple features.

Abstract: Semi-hidden Markov models (SHMMs) have been suggested and applied to isolated speaker-dependent E-set recognition. The SHMM differs from the conventionaJ hidden Markov model (HMM) in that its states can be classified into types. A function which detects signals corresponding to state types is thus included in the SHMMs and utilized to supervise the estimation of their parameters. This general structure is implemented in the recognition experiment as models with their states classified into stationary and transient types. The average recognition error rate is about 18.9% which compares favourably with the average of about 36.4% reported when using a dynamic time warping (DTW) recognition system by Lienard and Soong (ref 3) on an equivalent vocabulary. Tests using corresponding HMMs show similar results to that of the DTW system.

Conditional histogram vector quantization for spellmode recognizer

Abstract: In speech recognition, vector quantizers have traditionally been used as a pre-processor for sophisticated algorithms such as hidden Markov modelling (HMM) or dynamic time warping (DTW). Recently, simpler systems based more directly on vector quantization (VQ) have been proposed for recognizing isolated words with small vocabularies. The major problem with these simple algorithms is the lack of temporal information. This paper describes a conditional histogram technique which incorporates temporal information by considering the relative likelihoods that certain codewords follow others. Simulation results show that this approach produces better decoding results than the simple VQ algorithm with similar complexity.

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

Combining Synthetic Image Generation And Al To Aid Automatic Target Recognizers

Abstract: Recognition of real world targets in complex backgrounds under variable environmental conditions and operating states is presenting severe chal-lenges to designers of automatic target recognizers. This is primarily due to the lack of identifiable statistical invariants in the target/background signature. OAO Corporation has available a sophisticated signature prediction capability that can be used in conjunction with a natural language description of the recognition context to determine both the features and the feature strengths that are appropriate for the specified recognition context. Our signature prediction capability can be used to design a context adaptive target recognizer based either upon classical pattern recognition principles or more modern but less mature learning networks such as found in emerging neurocomputers.