2D Face Recognition

There is an increasing demand for biometric security systems in several fields. This study presents a highly accurate facial recognition method that uses high-speed transformation and facial morphing using region-limited log-polar transformation based on a center point calculated from the coordinates of both eyes and corners of the mouth. Log-polar transformation is limited to the region, so that the region including the feature can be suppressed to the minimum range, thereby facilitating high-speed transformation. Additionally, after facial morphing, the shapes of the eyes and mouth are altered based on the outline of the face, enabling high-precision facial recognition. The efficacy of the proposed method is verified experimentally. Therefore, we can confirm that 91.72% of images using the color FERET database and 96% of images using the FEI face database can be recognized using our method.

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Machine Learning for Biometrics

Salah

2010

Handbook of Research on Machine Learning Applications and Trends

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Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. We focus on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, we hope to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems.

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2D Face Recognition

Cited by 3 publications

References 71 publications

High Speed and High Precision Facial Recognition Using Log-Polar Transformation

High Speed and High Precision Facial Recognition Using Log-Polar Transformation

Log-Polar Facial Recognition Using Minimum Feature Band

Machine Learning for Biometrics

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