Face recognition: Some challenges in forensics

Jain, Anil K.; Klare, Brendan; Park, Unsang

doi:10.1109/fg.2011.5771338

Cited by 94 publications

(38 citation statements)

References 37 publications

(50 reference statements)

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“…Face recognition technology is being used in a number of applications, including de-duplication of identification cards, and verification of prisoner identities [6]. A major factor for the success of face recognition in these scenarios is the ability to mostly constrain factors known to be detrimental to face recognition performance, such as facial pose and expression, illumination, and background.…”

Section: Introductionmentioning

confidence: 99%

Face recognition across time lapse: On learning feature subspaces

Klare

Jain

2011

2011 International Joint Conference on Biometrics (IJCB)

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There is a growing interest in understanding the impact of aging on face recognition performance, as well as designing recognition algorithms that are mostly invariant to temporal changes. While some success has been made on this front, a fundamental questions has yet to be answered: do face recognition systems that compensate for the effects of aging compromise recognition performance for faces that have not undergone any aging? The studies in this paper help confirm that age invariant systems do seem to decrease performance in non-aging scenarios. This is demonstrated by performing training experiments on the largest face aging dataset studied in the literature to date (over 200,000 images from roughly 64,000 subjects). Further experiments conducted in this research help demonstrate the impact of aging on two leading commercial face recognition systems. We also determine the regions of the face that remain the most stable over time.

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Section: Introductionmentioning

confidence: 99%

Face recognition across time lapse: On learning feature subspaces

Klare

Jain

2011

2011 International Joint Conference on Biometrics (IJCB)

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“…An area where these kinds of systems have obtained an increased emphasis is the forensic field [2]. Forensic science analyses data collected by law enforcement agencies in order to prove or disapprove the guiltiness of a suspect with high confidence under the legal system.…”

Section: Introductionmentioning

confidence: 99%

Identification using face regions: Application and assessment in forensic scenarios

Tomé

Fiérrez

Vera-Rodríguez

et al. 2013

Forensic Science International

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription AbstractThis paper reports an exhaustive analysis of the discriminative power of the different regions of the human face on various forensic scenarios. In practice, when forensic examiners compare two face images, they focus their attention not only on the overall similarity of the two faces. They carry out an exhaustive morphological comparison region by region (e.g., nose, mouth, eyebrows, etc.). In this scenario it is very important to know based on scientific methods to what extent each facial region can help in identifying a person. This knowledge obtained using quantitative and statical methods on given populations can then be used by the examiner to support or tune his observations. In order to generate such scientific knowledge useful for the expert, several methodologies are compared, such as manual and automatic facial landmarks extraction, different facial regions extractors, and various distances between the subject and the acquisition camera. Also, three scenarios of interest for forensics are considered comparing mugshot and Closed-Circuit TeleVision (CCTV) face images using MORPH and SCface databases. One of the findings is that depending of the acquisition distances, the discriminative power of the facial regions change, having in some cases better performance than the full face.

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“…AFR is applied in forensics for face identification [68], face retrieval in still image databases or CCTV sequences [69], or for facial sketch recognition [70]. It could also help law enforcement through behaviour and facial expression observation [71], lie detection [72], lip tracking and reading [73].…”

Section: Securitymentioning

confidence: 99%

Automated Face Recognition: Challenges and Solutions

Olszewska¹

2016

Pattern Recognition - Analysis and Applications

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Automated face recognition (AFR) aims to identify people in images or videos using pattern recognition techniques. Automated face recognition is widely used in applications ranging from social media to advanced authentication systems. Whilst techniques for face recognition are well established, the automatic recognition of faces captured by digital cameras in unconstrained, real-world environment is still very challenging, since it involves important variations in both acquisition conditions as well as in facial expressions and in pose changes. Thus, this chapter introduces the topic of computer automated face recognition in light of the main challenges in that research field and the developed solutions and applications based on image processing and artificial intelligence methods.

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Face recognition: Some challenges in forensics

Cited by 94 publications

References 37 publications

Face recognition across time lapse: On learning feature subspaces

Face recognition across time lapse: On learning feature subspaces

Identification using face regions: Application and assessment in forensic scenarios

Automated Face Recognition: Challenges and Solutions

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