A face recognition system based on automatically determined facial fiducial points

Arca, Stefano; Campadelli, Paola; Lanzarotti, Raffaella

doi:10.1016/j.patcog.2005.06.015

Cited by 56 publications

(36 citation statements)

References 21 publications

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“…Regarding the former, we observe that there is a general agreement [16,19] that d eye ≤ 0.25 is a good criterion to identify correct eye detection. Regarding the latter, following the idea presented in [19], we studied the relationship between d eye and the face recognition rate of some baseline methods available in the CSU package [2] and our algorithm presented in [1].…”

Section: Resultsmentioning

confidence: 99%

Precise eye localization through a general-to-specific model definition

Campadelli¹,

Lanzarotti²,

Lipori³

2006

Procedings of the British Machine Vision Conference 2006

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Section: Resultsmentioning

confidence: 99%

Precise eye localization through a general-to-specific model definition

Campadelli¹,

Lanzarotti²,

Lipori³

2006

Procedings of the British Machine Vision Conference 2006

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“…Similar parts-based features are demonstrated for face recognition by [1,8,34]. Generation of the Tom-vs-Pete classifiers can also be viewed as a feature selection process where we seek features suitable for face verification.…”

Section: Related Workmentioning

confidence: 96%

Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification

Berg¹,

Belhumeur²

2012

Procedings of the British Machine Vision Conference 2012

124

128

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We propose a method of face verification that takes advantage of a reference set of faces, disjoint by identity from the test faces, labeled with identity and face part locations. The reference set is used in two ways. First, we use it to perform an "identity-preserving" alignment, warping the faces in a way that reduces differences due to pose and expression but preserves differences that indicate identity. Second, using the aligned faces, we learn a large set of identity classifiers, each trained on images of just two people. We call these "Tom-vs-Pete" classifiers to stress their binary nature. We assemble a collection of these classifiers able to discriminate among a wide variety of subjects and use their outputs as features in a same-or-different classifier on face pairs. We evaluate our method on the Labeled Faces in the Wild benchmark, achieving an accuracy of 93.10%, significantly improving on the published state of the art.

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“…Many different techniques have been developed to automatically tag facial landmarks on a face [1,2,3,4]. These techniques achieve good results over good quality and frontal faces, but are still not good enough for the cases of having high variability and low quality images.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the variability of facial landmarks in a forensic scenario

Vera-Rodríguez

Tomé

Fiérrez

et al. 2013

2013 International Workshop on Biometrics and Forensics (IWBF)

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: ABSTRACTThis paper reports an study of the variability of facial landmarks in a forensic scenario. This variability is affected by two factors: on the one hand, the precision in which the landmarks are tagged (manually or automatically), and on the other hand some other variability factors such as the pose, expression, occlusions, etc. For this study, a mugshot database of 50 persons has been collected following the procedure used by the Spanish Guardia Civil. Mugshots are taken with three distances between the persons and the camera (3, 2, 1 meters) showing the full body, the upper body and the face respectively, obtaining in total 1200 images. 21 facial landmarks are defined and the database was manually tagged imitating the procedure followed by a forensic examiner. This paper analyses the facial landmarking variability for the three distances considered, and also considering the differences obtained for male and female. Results show that landmarks located in the outer part of the face (highest end of the head, ears and chin) present a higher level of variability compared to the landmarks located the inner face (eye region, and nose). Regarding the gender, the landmarks placed in the outer part of the face present a higher level of variability for women compared to men.

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A face recognition system based on automatically determined facial fiducial points

Cited by 56 publications

References 21 publications

Precise eye localization through a general-to-specific model definition

Precise eye localization through a general-to-specific model definition

Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification

Analysis of the variability of facial landmarks in a forensic scenario

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