28 Development of a Hardware Benchmark for Forensic Face Detection Applications
Face detection techniques are valuable in the forensic investigation since they help criminal investigators to identify victims/offenders in child sexual exploitation material. Deep learning approaches proved successful in these tasks, but their high computational requirements make them unsuitable i...
| Authors: | , , , , , , |
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| Format: | book part |
| Publication Date: | 2021 |
| Country: | España |
| Institution: | Universidad de Castilla-La Mancha |
| Repository: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/28635 |
| Online Access: | http://doi.org/10.18239/jornadas_2021.34.28 http://hdl.handle.net/10578/28635 |
| Access Level: | Open access |
| Keyword: | Face Detection Benchmark GPU CPU |
| Summary: | Face detection techniques are valuable in the forensic investigation since they help criminal investigators to identify victims/offenders in child sexual exploitation material. Deep learning approaches proved successful in these tasks, but their high computational requirements make them unsuitable if there are time constraints. To cope with this problem, we use a resizing strategy over three face detection techniques —MTCNN, PyramidBox and DSFD— to improve their speed over samples selected from the WIDER Face and UFDD datasets across several CPUs and GPUs. The best speed-detection trade-off was achieved reducing the images to 50% of their original size and then applying DSFD. The fastest hardware for this purpose was a Nvidia GPU based on the Turing architecture. |
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