Face recognition technologies for evidential evaluation of video traces

Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future

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

FACE RECOGNITION WITH LOW FALSE POSITIVE ERROR RATE

Nowadays face recognition systems are widely used in the world. In China these systems are used in safe cities projects in production, in Russia they are used mostly in closed-loop systems like factories, business centers with biometric access control or stadiums. Closed loop means that we need to identify people from a fixed dataset: in factory it’s a list of employees, in stadium it’s a list of ticket owners. The most challenging task is to identify people from some large city with an open dataset: we don’t have a fixed set of people in the city, it’s rapidly changing due to migration. Another limit is the accuracy of the system: we can’t make a lot of false positive errors (when a person is incorrectly recognized as another person) because number of human operators is limited and they are expensive. We propose an approach to maximize face recognition accuracy for a fixed false positive error rate using limited amount of hardware

Face Image Quality Assessment: A Literature Survey

The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions

Objektivizacija vizualne prihvatljivosti degradacije originala fotografske slike = Objectification of the original photographic image degradation visual acceptability

Fotografska slika je najčešći medij prenošenja informacija dvodimenzionalnom statičnom slikom. Kako bi se osigurao odabir fotografskih slika koje prenose željene informacije, potrebno je osigurati objektivizaciju njihove vizualne prihvatljivosti. Odabir fotografskih slika koje prenose određene informacije, bez obzira ostvaruje li se ta slika kao samostalan medij ili kroz druge, medije, tiskane ili elektronske, primarno se temelji na vizualnim procjenama. Informacije koje prenosi fotografska slika određene su njezinom semantikom koja je definirana tehničkim i sintaktičkim aspektima. U disertaciji se provode istraživanja vizualnim uspoređivanjem kreiranih originala i obradom degradiranih originala fotografskih slika te mjernim određivanjem ukupne promjene boja, svjetline, kromatičnosti, dinamičkog raspona, vrijednosti histograma, sposobnosti razdvajanja linija i faktora sličnosti kao pokazatelja njihovih deskriptivnih karakteristika. Istraživanja upućuju na mogućnost definiranja granica prihvatljivosti promjena pojedinih tehničkih i sintaktičkih vrijednosti fotografske slike uz zadržavanje informacija koje prenosi, kao i mogućnost upravljanja pojedinim parametrima digitalnog zapisa fotografske slike sa svrhom mijenjanja informacije koja se prenosi uz zadržavanje ikoničkog karaktera konkretne fotografske slike odnosno prihvaćanja te slike kao realnog zapisa. U području promjena plavog, zelenog i crvenog kanala digitalnog zapisa fotografske slike u granicama zadržavanja ikoničkog karaktera, istraživanje dokazuje povezanost mjernih karakteristika boja fotografske slike i percepcije fotografske slike, ali i povezanost granica prihvatljivosti promjena vrijednosti kanala zapisa i motiva fotografske slike. Provedena istraživanja povezuju percepciju, odnosno vizualnu procjenu, fotografske slike s vrijednostima promijene dinamičkog raspona, ekspozicije, tonskih vrijednosti, ukupne promijene boja ∆Eₒₒ, svjetline i sposobnosti razdvajanja linija kao parametara objektivne procjene promjena definiranih fotografskih slika u odnosu na originalnu fotografsku sliku, te ukazuju na povezanost navedenih mjernih karakteristika fotografske slike, faktora sličnosti te vizualnih ekspertnih i procjena šire skupine konzumenata. Potvrđuju mogućnost objektivizacije procjene degradacije originala fotografske slike, odnosno prihvatljivosti te degradacije, a da degradirana fotografska slika zadržava ikoničnost u odnosu na originalnu fotografsku sliku