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 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.