Spectral eigenface representation for human identification

Abstract EN

Human identification based on face images, as physical biometric means, plays an imperative role in many applications area.

The methods for human identification using face image uses either part of the face, all face, or mixture from these methods, in either time domain or frequency domain.

This paper investigate the ability to implement the eigenface in frequency domain, the result spectral eigenface is utilize as a feature vector means for human identification.

The converting from eigenface implementation in time domain, into spectral eigenface implementation in frequency domain, is based on implemented the correlation by using FFT.

The Min-max is invoked as normalization techniques that increase spectral eigenface robustness to variations in facial geometry and illumination.

Two face images are contrast in terms of their correlation distance.

A threshold (10.50x107) is used to restrict the impostor face image from being identified.

The experimental results point up the effectiveness of a new method in either using varying (noisy images, unknown image, face expressions, illumine, and scale s), with identification value of 100%.