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Quality metrics for practical face recognition
In biometric studies, quality evaluation of input data is very important, and has proven to have a direct relation with system performance. Quality measures can provide real-time feedback to reduce the number of poor quality submissions to the system. Another benefit is that they can predict and imp...
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Main Authors: | , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | In biometric studies, quality evaluation of input data is very important, and has proven to have a direct relation with system performance. Quality measures can provide real-time feedback to reduce the number of poor quality submissions to the system. Another benefit is that they can predict and improve the authentication performance (e.g., by using quality-dependent thresholds). This paper main focus is image quality assessment for face recognition. First, we evaluate a number of techniques that measure image quality factors namely, contrast, brightness, focus, sharpness, and illumination. Second, via a set of experiments measuring the sensitivity of each matric to quality change, we select the most practical measure(s) for each quality factor. Finally, we propose a novel face image quality index (FQI) that combines the five aforementioned quality factors. Via a set of statistical significance tests, we illustrate and support that FQI is a promising quality measure that can be used as an alternative to some benchmark face image quality measures. |
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ISSN: | 1051-4651 2831-7475 |