Ear identification based on surveillance camera images

Hoogstrate, A.J.; Heuvel, H. van den; Huyben, E.

doi:10.1016/s1355-0306(01)71885-0

Cited by 63 publications

(32 citation statements)

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“…Customarily, single facial features are first sought out, e.g., moles, scars or ear morphology [9,10]. Anthropometric variables may also be deduced from the imagery, although careful attention must be paid to the above issues of camera angles and distances [11][12][13].…”

Section: Discussionmentioning

confidence: 99%

Facial recognition and laser surface scan: a pilot study

Lynnerup

Clausen

Kristoffersen

et al. 2009

Forensic Sci Med Pathol

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Surface scanning of the face of a suspect is presented as a way to better match the facial features with those of a perpetrator from CCTV footage. We performed a simple pilot study where we obtained facial surface scans of volunteers and then in blind trials tried to match these scans with 2D photographs of the faces of the volunteers. Fifteen male volunteers were surface scanned using a Polhemus FastSCAN Cobra Handheld Laser Scanner. Three photographs were taken of each volunteer's face in full frontal, profile and from above at an angle of 45 degrees and also 45 degrees laterally. Via special software (MIMICS and Photoshop) the surface scans were matched with the photographs in blind trials. The matches were graded as: a good fit; possible fit; and no fit. All the surface scans and photos were matched correctly, although one surface scan could be matched with two angled photographs, meaning that the discriminatory value was 86.7%. We also tested the surface scanner in terms of reliability in establishing point measures on skulls, and compared with physical measurements performed by calipers. The variation was on average 1 mm for five cranial measures. We suggest how surface scanning might be applied in forensic facial identification.

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Section: Discussionmentioning

confidence: 99%

Facial recognition and laser surface scan: a pilot study

Lynnerup

Clausen

Kristoffersen

et al. 2009

Forensic Sci Med Pathol

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“…Nowadays the observation of characteristics is a standard technique in forensic investigation and has been used as evidence in hundreds of cases. The strength of this evidence has, however, also been called into question by courts in the Netherlands [10]. In order to study the strength of ear prints as evidence, the Forensic Ear identification Project (FearID) was initiated by nine institutes from Italy, the UK, and the Netherlands in 2006.…”

Section: Figure 1: Characteristics Of the Human Ear The German Criminmentioning

confidence: 99%

Ear biometrics: a survey of detection, feature extraction and recognition methods

2012

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The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features.The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people.Because of these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, we categorize and summarize approaches to ear detection and recognition in 2D and 3D images. Then, we provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which we consider to be the most important application of ear recognition characteristic in the near future.

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“…Attarchi et al [6] locate the outer contour of the ear by searching for the longest connected edge in the edge image. Hoogstrate et al [21] and Pun and Moon [35] have made significant progress in using ear detection techniques to identify targets in surveillance camera footage. Arbab and Nixon [4] have used the Hough transform for enhancing regions with a high density of edges.…”

Section: Introductionmentioning

confidence: 99%

Entropy-cum-Hough-transform-based ear detection using ellipsoid particle swarm optimization

Prajwal

Srinivas

Manikantan

et al. 2015

Machine Vision and Applications

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Ear detection in facial images under varying pose, background and occlusion is a challenging issue. This paper proposes an entropy-cum-Hough-transform-based approach for enhancing the performance of an ear detection system, employing the unique combination of hybrid ear localizer (HEL) and ellipsoid ear classifier (EEC). By exploiting the entropic properties of the ear, as well as its ellipsoid structure, the HEL identifies the most probable location of the ear. To curb false ear acceptances by the HEL, the EEC verifies the actual presence of an ear in facial images, by attempting to fit an ellipse on the localized ear. EEC performs this by using the ellipsoid parametric set optimized by the evolutionary ellipsoid particle swarm optimization technique. The amount of success met by the EEC in doing so, i.e., the 'goodness of fit', is used to verify the presence of an ear. Experiments have been carried out on five benchmark face databases, namely Color FERET, Pointing Head Pose, UMIST, CMU-PIE, and FEI. The results show the efficiency and robustness of both HEL and EEC working individually and as a system, for enhanced ear detection.

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Ear identification based on surveillance camera images

Cited by 63 publications

References 1 publication

Facial recognition and laser surface scan: a pilot study

Facial recognition and laser surface scan: a pilot study

Ear biometrics: a survey of detection, feature extraction and recognition methods

Entropy-cum-Hough-transform-based ear detection using ellipsoid particle swarm optimization

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