ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM

Ali Rıza Yılmaz; Burcu Erkmen; Oğuzhan Yavuz

ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM

Abstract

In this work, intelligent algorithms designed on embedded hardware for signature recognition is presented. Feed forward Conic Section Function Neural Network (CSFNN) and Differential Evaluation Algorithm (DEA) are implemented on the Field Programmable Gate Arrays (FPGAs). Unified robust classifier CSFNN is applied on the preprocessed signatures for recognition purpose. DEA is used for training CSFNN in order to overcome local minimum problems. The implemented CSFNN on FPGA has the characteristics of flexible adaptable size providing various datasets. The CSFNN implementation on FPGA is realized using the 16-bit floating point arithmetic IEEE 754-2008 standard. The proposed on-chip CSFNN based signature recognition system described in VHDL has been implemented and evaluated on a high–end Virtex 7 -VC707 platform. The intelligent system embedded on FPGA is approximately 105 times faster than its equivalent software implementation.

Keywords

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Ali Rıza Yılmaz This is me
Türkiye

Burcu Erkmen This is me
Türkiye

Oğuzhan Yavuz This is me
Türkiye

Publication Date

September 1, 2016

Submission Date

February 22, 2016

Acceptance Date

June 25, 2016

Published in Issue

Year 2016 Volume: 34 Number: 3

IZ

https://izlik.org/JA98MH35RU

Cite

RIS / Bibtex

APA

Yılmaz, A. R., Erkmen, B., & Yavuz, O. (2016). ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM. Sigma Journal of Engineering and Natural Sciences, 34(3), 393-405. https://izlik.org/JA98MH35RU

AMA

1.Yılmaz AR, Erkmen B, Yavuz O. ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM. SIGMA. 2016;34(3):393-405. https://izlik.org/JA98MH35RU

Chicago

Yılmaz, Ali Rıza, Burcu Erkmen, and Oğuzhan Yavuz. 2016. “ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM”. Sigma Journal of Engineering and Natural Sciences 34 (3): 393-405. https://izlik.org/JA98MH35RU.

EndNote

Yılmaz AR, Erkmen B, Yavuz O (September 1, 2016) ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM. Sigma Journal of Engineering and Natural Sciences 34 3 393–405.

IEEE

[1]A. R. Yılmaz, B. Erkmen, and O. Yavuz, “ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM”, SIGMA, vol. 34, no. 3, pp. 393–405, Sept. 2016, [Online]. Available: https://izlik.org/JA98MH35RU

ISNAD

Yılmaz, Ali Rıza - Erkmen, Burcu - Yavuz, Oğuzhan. “ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM”. Sigma Journal of Engineering and Natural Sciences 34/3 (September 1, 2016): 393-405. https://izlik.org/JA98MH35RU.

JAMA

1.Yılmaz AR, Erkmen B, Yavuz O. ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM. SIGMA. 2016;34:393–405.

MLA

Yılmaz, Ali Rıza, et al. “ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM”. Sigma Journal of Engineering and Natural Sciences, vol. 34, no. 3, Sept. 2016, pp. 393-05, https://izlik.org/JA98MH35RU.

Vancouver

1.Ali Rıza Yılmaz, Burcu Erkmen, Oğuzhan Yavuz. ACCELERATING HANDWRITTEN SIGNATURE RECOGNITION USING INTELLIGENT ALGORITHM BASED EMBEDDED SYSTEM. SIGMA [Internet]. 2016 Sep. 1;34(3):393-405. Available from: https://izlik.org/JA98MH35RU