Blind Image Quality Assessment for Face Pose Problem

No-Reference image quality assessment for face images is of high interest since it can be required for biometric systems such as biometric passport applications to increase system performance. This can be achieved by controlling the quality of biometric sample images during enrollment. This paper proposes a novel no-reference image quality assessment method that extracts several image features and uses data mining techniques for detecting the pose variation problem in facial images. Using subsets from three public 2D face databases PUT, ENSIB, and AR, the experimental results recorded a promising accuracy of 97.06% when using the RandomForest Classifier, which outperforms other classifier

Evaluation and Understandability of Face Image Quality Assessment

Face image quality assessment (FIQA) has been an area of interest to researchers as a way to improve the face recognition accuracy. By filtering out the low quality images we can reduce various difficulties faced in unconstrained face recognition, such as, failure in face or facial landmark detection or low presence of useful facial information. In last decade or so, researchers have proposed different methods to assess the face image quality, spanning from fusion of quality measures to using learning based methods. Different approaches have their own strength and weaknesses. But, it is hard to perform a comparative assessment of these methods without a database containing wide variety of face quality, a suitable training protocol that can efficiently utilize this large-scale dataset. In this thesis we focus on developing an evaluation platfrom using a large scale face database containing wide ranging face image quality and try to deconstruct the reason behind the predicted scores of learning based face image quality assessment methods. Contributions of this thesis is two-fold. Firstly, (i) a carefully crafted large scale database dedicated entirely to face image quality assessment has been proposed; (ii) a learning to rank based large-scale training protocol is devel- oped. Finally, (iii) a comprehensive study of 15 face image quality assessment methods using 12 different feature types, and relative ranking based label generation schemes, is performed. Evalua- tion results show various insights about the assessment methods which indicate the significance of the proposed database and the training protocol. Secondly, we have seen that in last few years, researchers have tried various learning based approaches to assess the face image quality. Most of these methods offer either a quality bin or a score summary as a measure of the biometric quality of the face image. But, to the best of our knowledge, so far there has not been any investigation on what are the explainable reasons behind the predicted scores. In this thesis, we propose a method to provide a clear and concise understanding of the predicted quality score of a learning based face image quality assessment. It is believed that this approach can be integrated into the FBI’s understandable template and can help in improving the image acquisition process by providing information on what quality factors need to be addressed

Performance evaluation of no-reference image quality metrics for face biometric images

The accuracy of face recognition systems is significantly affected by the quality of face sample images. The recent established standardization proposed several important aspects for the assessment of face sample quality. There are many existing no-reference image quality metrics (IQMs) that are able to assess natural image quality by taking into account similar image-based quality attributes as introduced in the standardization. However, whether such metrics can assess face sample quality is rarely considered. We evaluate the performance of 13 selected no-reference IQMs on face biometrics. The experimental results show that several of them can assess face sample quality according to the system performance. We also analyze the strengths and weaknesses of different IQMs as well as why some of them failed to assess face sample quality. Retraining an original IQM by using face database can improve the performance of such a metric. In addition, the contribution of this paper can be used for the evaluation of IQMs on other biometric modalities; furthermore, it can be used for the development of multimodality biometric IQMs

Performance evaluation of no-reference image quality metrics for face biometric images

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