Thumwarin, Pitak

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Thumwarin, Pitak
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Thumwarin, P.
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pitak.th@kmitl.ac.th

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    Firearm identification based on FIR system characterizing rotation invariant feature of cartridge case image
    (2008-12-01)
    Thumwarin, P.
    ;
    Prasit, C.
    ;
    Boonbumroong, P.
    ;
    Matsuura, T.
    We propose firearm identification method based on FIR system characterizing rotation invariant feature of cartridge case image. The rotation invariant feature is represented by the absolute value of Fourier coefficients of polar image on circles with different radii of the cartridge case's primer. Then the FIR(Finite impulse response) system characterizing the rotation invariant feature of cartridge case's primer image is introduced. The absolute value of Fourier coefficients obtained from the polar image on the circles with different radii are used as the input and output of the FIR system, respectively. The obtained impulse response of the FIR system is considered as the unique feature of the individual gun. Finally, a firearm can be identified by using the Fisher's linear discriminant function of the obtained impulse response of the FIR system. The firearm identification experiments are conducted to show the effectiveness of the proposed method. © 2008 IEEE.
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    Firearm identification system with rotation invariance
    (2010-01-01)
    Thumwarin, P.
    ;
    Prasit, C.
    ;
    Yakoompai, K.
    ;
    Matsuura, T.
    This paper presents a firearm identification method with rotation invariance. The rotation invariant feature can be extracted by the magnitude of Fourier coefficients of polar image of the cartridge on circles with different radii. Then the fluctuation of the obtained magnitude of Fourier coefficients can be reduced by the FIR(Finite impulse response) Wiener filter. And they are used as the input and the output of the FIR system characterizing the rotation invariant feature of cartridge image. The impulse response of the FIR system is used as the unique feature for firearm identification. Finally, the firearm can be identified by the Fisher's linear discriminant function. The experimental results are given to show the effectiveness of the proposed method. ©ICROS.
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    On-line signature verification based on FIR system characterizing velocity and direction change of barycenter trajectory
    (2010-12-01)
    Thumwarin, P.
    ;
    Pernwong, J.
    ;
    Wakayaphattaramanus, N.
    ;
    Matsuura, T.
    We propose an on-line signature verification method based on finite impulse response(FIR) system characterizing velocity and direction change of barycenter trajectory. First, the discrete cosine transforms (DCTs) of the characteristics are used to reduce fluctuation and extract the feature of handwriting in signing process. Then the signature verification system is realized by the three FIR subsystems. The obtained impulse responses of the three FIR subsystems are used as the individual feature for signature verification. Signature can be verified by evaluating the difference between the impulse responses of the FIR subsystems for a reference signature and the signature to be verified. The signature verification experiments were performed on the SUBCORPUS-100 MCYT signature database[6] consisting of 5,000 signatures from 100 signers. ©2010 IEEE.
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    On-line writer identification based on handwriting velocity and curvature of script
    (2008-12-01)
    Matsuura, T.
    ;
    Thumwarin, P.
    ;
    Kato, N.
    We propose an on-line writer identification method based on the handwriting velocity and curvature of script. First, we calculate the autocorrelation functions for the normalized handwriting velocity and the normalized curvature of script, respectively. Secondly, we realize a finite impulse response (FIR) system having the Fourier coefficients of the obtained autocorrelation functions as the input and output, respectively. The realized FIR system can characterize the relation between the handwriting velocity and curvature of script. Writer can be identified by evaluating the difference between the impulse responses of the FIR systems for the writer and the reference. Finally, writer identification experiments are given to show the effectiveness of the proposed method. © 2008 IEEE.
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    On-line signature verification based on FIR system characterizing motion pressure
    (2011-12-01)
    Thumwarin, P.
    ;
    Pernwong, J.
    ;
    Matsuura, T.
    We propose an on-line signature verification method based on finite impulse response(FIR) system characterizing motion pressure of pen-point trajectory in signing process. The discrete cosine transforms(DCTs) of the handwriting feature is used to reduce the fluctuation and extract the feature of handwriting. The signature verification system is realized by four FIR subsystems. Then motion pressure can be characterized by using the FIR subsystem having the DCTs of the area pressure and motion pressure as the input and the output of the FIR system, respectively. The obtained impulse responses of the four FIR subsystems are used as the individual feature for signature verification. Finally, the signature can be verified by evaluating the difference between the impulse responses of the FIR subsystems for a reference signature and the signature to be verified. The signature verification experiments were performed on the SUBCORPUS-100 MCYT [J. Ortega-Garcia et al. 2003] signature database consisting of 5,000 signatures from 100 signers. The propose method yielded equal error rate(EER) 4.38 % on skilled forgeries. © 2011 ACM.
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    Gait identification based on FIR system characterizing motion of leg
    (2013-12-01)
    Thumwarin, P.
    ;
    Srisuk, K.
    ;
    Matsuura, T.
    This paper presents gait identification method based on Finite Impulse Response (FIR) system characterizing motion of leg. First, the four gait features, area of footstep, angle of footstep, height and width of the human silhouette image, are calculated from the human silhouette image. Then they are expanded into Fourier series to reduce the fluctuation of human body motion. The motion of leg can be characterized by two FIR gait identification systems. For the first FIR system, the Fourier coefficients of the width of human silhouette image and the area of footstep are used as input and output of the system, respectively. For the second FIR system, the Fourier coefficients of the height of the human silhouette image and the angle of footstep are used as input and output of the system, respectively. The obtained impulse responses of the two FIR systems are used as the individual feature for gait identification. The gait identification experiments were performed on CASIA GAIT Dataset B [6], which contains 8,184 gait data for 11 view angles from 124 persons. The average of error rates obtained from 90° view angle was 3.48%. © 2013 IEEE.