A practical case study: Face recognition on low quality images using gabor wavelet and support vector machines

Face recognition is a problem that arises on many real world applications, such as those related with Ambient Intelligence (AmI). The specific nature and goals of AmI applications, however, requires minimizing the invasiveness of data collection methods, often resulting in a drastic reduction of data quality and a plague of unforeseen effects which can put standard face recognition systems out of action. In order to deal with this, a face recognition system for AmI applications must not only be carefully designed but also subject to an exhaustive configuration plan to ensure it offers the required accuracy, robustness and real-time performance. This document covers the design and tuning of a holistic face recognition system targeting an Ambient Intelligence scenario. It has to work under partially uncontrolled capturing conditions: frontal images with pose variation up to 40 degrees, changing illumination, variable image size and degraded quality. The proposed system is based on Support Vector Machine (SVM) classifiers and applies Gabor Filters intensively. A complete sensitivity analysis shows how the recognition accuracy can be boosted through careful configuration and proper parameter setting, although the most adequate setting depends on the requirements for the final system.