Implementation of An RBF Neural Network on Embedded Systems : Real-Time Face Tracking and Identity Verification  

Oleh:

Yang, Fan ; Paindavoine, M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 5 (2003), page 1162-1175.
Topik: VERIFICATION; RBF neural network; systems; real - time; face tracing; identity verification

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.9 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper describes a real time vision system that allows us to localize faces in video sequences and verify their identity. These processes are image processing techniques based on the radial basis function (RBF) neural network approach. The robustness of this system has been evaluated quantitatively on eight video sequences. We have adapted our model for an application of face recognition using the Olivetti Research Laboratory (ORL), Cambridge, UK, database so as to compare the performance against other systems. We also describe three hardware implementations of our model on embedded systems based on the field programmable gate array (FPGA), zero instruction set computer (ZISC) chips, and digital signal processor (DSP) TMS320C62, respectively. We analyze the algorithm complexity and present results of hardware implementations in terms of the resources used and processing speed. The success rates of face tracking and identity verification are 92 % (FPGA), 85 % (ZISC), and 98.2 % (DSP), respectively. For the three embedded systems, the processing speeds for images size of 288 × 352 are 14 images / s, 25 images / s, and 4.8 images / s, respectively.

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