Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification

• Published in: IEEE transactions on neural networks Vol. 14; no. 5; pp. 1162 - 1175
• Main Authors: Fan Yang, Paindavoine, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2003
• Subjects: Digital signal processing chips; Embedded system; Face detection; Field programmable gate arrays; Hardware; Image processing; Machine vision; Neural networks; Real time systems; Video sequences
• ISSN: 1045-9227; 1941-0093
• DOI: 10.1109/TNN.2003.816035

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 /spl times/ 352 are 14 images/s, 25 images/s, and 4.8 images/s, respectively.