New method for height estimation of subjects represented in photograms taken from video surveillance systems

Angelis, Danilo De; Sala, R.; Cantatore, Angela; Poppa, Pasquale; Dufour, M.; Grandi, Marco; Cattaneo, Cristina

doi:10.1007/s00414-007-0176-4

Cited by 47 publications

(21 citation statements)

References 13 publications

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“…This makes them particularly useful in applications such as video surveillance, where they are directly compatible with how humans perceive their surroundings [210], [67], [43], [204], [47], [257], [45], [46]. In other words, when a human attempts to verbally describe a person, obvious characteristics regarding the person's appearance such as gender, age, height and clothes color are often used (e.g., in police reports).…”

Section: Figmentioning

confidence: 99%

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Dantcheva

Elia

Ross

2016

IEEE Trans.Inform.Forensic Secur.

344

224

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Recent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., "young Asian female with dark eyes and brown hair"). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.

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Section: Figmentioning

confidence: 99%

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Dantcheva

Elia

Ross

2016

IEEE Trans.Inform.Forensic Secur.

344

224

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“…Final positive identification can subsequently be performed by other more specific methods in the field of anthroposcopy, anthropometry, 2D/2D or 2D/3D superimposition of images, 2D/3D image comparisons [6][7][8][9][10][11][12][13][14], along with other factors such as height estimation [15] and gait analysis [16,17]. These methods are widely applied, in spite of the fact that extensive and conclusive validation tests for these methods are lacking nor do accredited guidelines exist apart from an attempt by the German AGIB group [18].…”

Section: Introductionmentioning

confidence: 99%

A new atlas for the evaluation of facial features: advantages, limits, and applicability

et al. 2010

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Methods for the verification of the identity of offenders in cases involving video-surveillance images in criminal investigation events are currently under scrutiny by several forensic experts around the globe. The anthroposcopic, or morphological, approach based on facial features is the most frequently used by international forensic experts. However, a specific set of applicable features has not yet been agreed on by the experts. Furthermore, population frequencies of such features have not been recorded, and only few validation tests have been published. To combat and prevent crime in Europe, the European Commission funded an extensive research project dedicated to the optimization of methods for facial identification of persons on photographs. Within this research project, standardized photographs of 900 males between 20 and 31 years of age from Germany, Italy, and Lithuania were acquired. Based on these photographs, 43 facial features were described and evaluated in detail. These efforts led to the development of a new model of a morphologic atlas, called DMV atlas ("Düsseldorf Milan Vilnius," from the participating cities). This study is the first attempt at verifying the feasibility of this atlas as a preliminary step to personal identification by exploring the intra- and interobserver error. The analysis yielded mismatch percentages from 19% to 39%, which reflect the subjectivity of the approach and suggest caution in verifying personal identity only from the classification of facial features. Nonetheless, the use of the atlas leads to a significant improvement of consistency in the evaluation.

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“…The examination of the face of individuals represented on pictures or videos from video surveillance systems can provide relevant information for identification, along with other body characteristics such as height estimation [1] and gait analysis [2].…”

Section: Introductionmentioning

confidence: 99%