Face Recognition Thesis 2012

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Finally, we propose Tied Joint Bayesian Face algorithm and Tied Joint PLDA to address large pose variations in the data, which drastically decreases performance in most existing face recognition algorithms.

To provide sufficient training images with large pose difference, we introduce a new database called the UCL Multi-pose database.

Once eyes are detected, the algorithm might then attempt to detect facial regions including eyebrows, the mouth, nose, nostrils and the iris.

Once the algorithm surmises that it has detected a facial region, it can then apply additional tests to validate whether it has, in fact, detected a face.

In order to work, face detection applications use machine learning and formulas known as algorithms to detecting human faces within larger images.

These larger images might contain numerous objects that aren’t faces such as landscapes, buildings and other parts of humans (e.g. Face detection is a broader term than face recognition.

This thesis concerns face recognition in uncontrolled environments in which the images used for training and test are collected from the real world instead of laboratories.

Compared with controlled environments, images from uncontrolled environments contain more variation in pose, lighting, expression, occlusion, background, image quality, scale, and makeup.

One of the most important applications of face detection, however, is facial recognition.

Face recognition describes a biometric technology that goes way beyond recognizing when a human face is present.


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