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EURASIP Journal on Embedded Systems
EURASIP Journal on Embedded Systems Cover Image
  • Research Article
  • Open Access

Real-Time Video Convolutional Face Finder on Embedded Platforms

EURASIP Journal on Embedded Systems20072007:021724

https://doi.org/10.1155/2007/21724

©  Franck Mamalet et al. 2007

  • Received: 27 April 2006
  • Accepted: 26 December 2006
  • Published:

Abstract

A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF) algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

Keywords

  • Feature Extraction
  • False Alarm Rate
  • Face Detection
  • Memory Allocation
  • Code Size

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Authors’ Affiliations

(1)
France Telecom Research and Development Division, 28 Chemin du Vieux Chêne, Meylan, 38243, France

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Copyright

© Franck Mamalet et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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