Facial Image Verification and Quality Assessment System -FaceIVQA

Although several techniques have been proposed for predicting biometric system performance using quality values, many of the research works were based on no-reference assessment technique using a single quality attribute measured directly from the data. These techniques have proved to be inappropriate for facial verification scenarios and inefficient because no single quality attribute can sufficient measure the quality of a facial image. In this research work, a facial image verification and quality assessment framework (FaceIVQA) was developed. Different algorithms and methods were implemented in FaceIVQA to extract the faceness, pose, illumination, contrast and similarity quality attributes using an objective full-reference image quality assessment approach. Structured image verification experiments were conducted on the surveillance camera (SCface) database to collect individual quality scores and algorithm matching scores from FaceIVQA using three recognition algorithms namely principal component analysis (PCA), linear discriminant analysis (LDA) and a commercial recognition SDK. FaceIVQA produced accurate and consistent facial image assessment data. The Result shows that it accurately assigns quality scores to probe image samples. The resulting quality score can be assigned to images captured for enrolment or recognition and can be used as an input to quality-driven biometric fusion systems.DOI:http://dx.doi.org/10.11591/ijece.v3i6.503

Long range facial image acquisition and quality

Abstract This chapter introduces issues in long range facial image acquisition and measures for image quality and their usage. Section 1, on image acquisition for face recognition discusses issues in lighting, sensor, lens, blur issues, which impact short-range biometrics, but are more pronounced in long-range biometrics. Section 2 introduces the design of controlled experiments for long range face, and why they are needed. Section 3 introduces some of the weather and atmospheric effects that occur for long-range imaging, with numerous of examples. Section 4 addresses measurements of “system quality”, including image-quality measures and their use in prediction of face recognition algorithm. That section introduces the concept of failure prediction and techniques for analyzing different “quality ” measures. The section ends with a discussion of post-recognition ”failure prediction ” and its potential role as a feedback mechanism in acquisition. Each section includes a collection of open-ended questions to challenge the reader to think about the concepts more deeply. For some of the questions we answer them after they are introduced; others are left as an exercise for the reader. 1 Image Acquisition Before any recognition can even be attempted, they system must acquire an image of the subject with sufficient quality and resolution to detect and recognize the face. The issues examined in this section are the sensor-issues in lighting, image/sensor resolution issues, the field-of view, the depth of field, and effects of motion blur

Predicting biometric facial recognition failure with similarity surfaces and support vector machines

The notion of quality in biometric system evaluation has often been restricted to raw image quality, with a prediction of failure leaving no other option but to acquire another sam-ple image of the subject at large. The very nature of this sort of failure prediction is very limiting for both identifying sit-uations where algorithms fail, and for automatically compen-sating for failure conditions. Moreover, when expressed in a ROC curve, image quality paints an often misleading picture regarding its potential to predict failure. In this paper, we extend previous work on predicting al-gorithmic failures via similarity surface analysis. To generate the surfaces used for comparison, we define a set of new fea-tures derived from distance measures or similarity scores from a recognition system. For learning, we introduce support vec-tor machines as yet another approach for accurate classifica-tion. A large set of scores from facial recognition algorithms are evaluated, including EBGM, Robust PCA, Robust Revo-cable PCA, and a leading commercial algorithm. Experimen-tal results show that we can reliably predict biometric system failure using the SVM approach. 1