SCIENCE@home

Predicting performance of a face recognition system based on image quality

Abhishek Dutta

Published 2015 in arXiv.org

ABSTRACT

In this dissertation, we present a generative model to capture the relation between facial image quality features (like pose, illumination direction, etc) and face recognition performance. Such a model can be used to predict the performance of a face recognition system. Since the model is based solely on image quality features, performance predictions can be done even before the actual recognition has taken place thereby facilitating many preemptive action. A practical limitation of such a data driven generative model is the limited nature of training data set. To address this limitation, we have developed a Bayesian approach to model the distribution of recognition performance measure based on the number of match and non-match scores in small regions of the image quality space. Random samples drawn from these models provide the initial data essential for training the generative model. Many automatic face recognition systems use automatically detected eye coordinates for facial image registration. We investigate the influence of automatic eye detection error on the performance of face recognition systems. This study helps us understand how image quality variations can amplify its influence on recognition performance by having dual impact on both facial image registration and facial feature extraction/comparison stages of a face recognition system. A forensic case involving face recognition commonly contains a surveillance view trace (usually a frame from CCTV footage) and a frontal suspect reference set containing facial images of suspects narrowed down by police and forensic investigation. In such forensic cases, we investigate if it is potentially more useful to apply a view based approach which involves adapting the frontal view reference set such that it matches the pose of the surveillance view trace.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-77 of 77 references · Page 1 of 1

CITED BY