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Real-Time Video Convolutional Face Finder on Embedded Platforms

EURASIP Journal on Embedded Systems volume 2007, Article number: 021724 (2007) Cite this article

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.

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Authors and Affiliations

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

    Franck Mamalet, Sébastien Roux & Christophe Garcia

Authors
  1. Franck Mamalet
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  2. Sébastien Roux
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  3. Christophe Garcia
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Correspondence to Franck Mamalet.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mamalet, F., Roux, S. & Garcia, C. Real-Time Video Convolutional Face Finder on Embedded Platforms. J Embedded Systems 2007, 021724 (2007). https://doi.org/10.1155/2007/21724

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

Keywords

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