An Exploration of Hardware Architectures for Face Detection:Conclusion

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Conclusion

In this chapter we examined various architectures for hardware face detection. We have seen how each implementation varies, in terms of the frame rate, hardware resources, design methodology and most importantly, effectiveness. The presented architectures and designs show that hardware face detection can achieve very high detection frame rates. The gain in speed results also in a very interesting observation; face detection usually is part of a higher-level problem, such as face recognition, demo- graphics, biomedical imaging applications, etc. By speeding up the face detection process, we can design complete systems either completely in hardware or as an embedded platform. Results from this chapter show that hardware face detection can achieve real-time frame rate detection, ranging from 24 to 52 fps depending on the chosen architecture.

The porting of algorithms such as face detection in hardware is a significant step toward the design of artificial intelligence. The algorithms used to detect faces can also be used to detect other objects as well. Pattern recognition in general has been a fundamental in artificial intelligence, and as technology progresses, more artificial intelligence algorithms can be ported in hardware, with the inherent benefits applied to several applications. Medical, control, security, space and aeronautics, and several other high end applica- tions can benefit from hardware implementation of pattern recognition algorithms. Face detection, while a small part of pattern recognition, is one of the first and most fundamental algorithms, and as such provides opportunities for expanding the hardware architectures to implement such algorithms.

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