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Quantification of Orofacial Phenotypes in Xenopus
Published on: November 6, 2014
Decision optimization for face recognition based on an alternate correlation plane quantification metric.
A Alfalou1, C Brosseau, P Katz
1ISEN Brest, Département Optoélectronique, L@bISEN, Brest, France. ayman.al‑falou@isen.fr
Optics Letters
|May 5, 2012
Summary
This study introduces a novel denoising method for correlation planes to improve VanderLugt correlator performance. By removing noise before analysis, it enhances discrimination and reduces false alarms in applications like face recognition.
Area of Science:
- Optical information processing
- Image processing and pattern recognition
Background:
- VanderLugt correlators are crucial for pattern recognition but can suffer from noise.
- Existing methods focus on filter optimization or peak detection, not correlation plane quality.
Purpose of the Study:
- To enhance the discrimination performance of VanderLugt correlators.
- To introduce a denoising technique for correlation planes prior to applying the peak-to-correlation energy (PCE) criterion.
Main Methods:
- A linear functional model represents correlation planes as combinations of peak, noise, and residuals.
- Singular value decomposition and orthonormalized functions model the correlation peak.
- Training data is used to identify and remove correlation noise components.
Main Results:
- The proposed technique effectively denoises correlation planes, reducing noise magnitude for true correlations.
- It significantly decreases the false alarm rate for non-target images.
- Tested with composite filters and face recognition, demonstrating effectiveness.
Conclusions:
- Denoising the correlation plane is a viable strategy for improving correlator performance.
- This method offers a robust enhancement independent of the specific correlation filter used.
- The technique shows promise for real-world applications like face recognition.
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