Periocular region appearance cues for biometric identification

Woodard, Damon L.; Pundlik, Shrinivas; Lyle, Jamie R.; Miller, Philip E.

doi:10.1109/cvprw.2010.5544621

Cited by 97 publications

(68 citation statements)

References 15 publications

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“…Periocular region includes the iris [10], eyes, eyelids, eye lashes, and part of the eyebrows [13]. Recognition using periocular biometrics is an emerging research area.…”

Section: Survey Of Periocular Biometrics Researchmentioning

confidence: 99%

“…They experimented with manually generated periocular data from a subset of high resolution frontal images of FRGC [1] with neutral expression, less appearance variations and controlled illumination. Woodard et al [13] used the LBP features and color histograms to represent the local appearance of the periocular region. They achieved better performance by using city block distance for matching LBP features and Bhattacharya coefficient for matching color histograms.…”

Section: Survey Of Periocular Biometrics Researchmentioning

confidence: 99%

“…In contrast to the previous approaches [13,14] which selected only the best frames with the same scale and rotation for matching, we perform rotation and scale normalization. In each video frame (NIR or visible), we automatically detect the eye pair by using a Haar feature based ensemble of weak classifiers as proposed by Viola and Jones [36].…”

Section: Automatic Periocular Region Detection In Videosmentioning

confidence: 99%

“…Previous works on periocular biometrics are mostly based on single image matching [11,12,13,14,15,16] where a single best frame per subject is handpicked to form the gallery. Each image in the query sequence is matched with the gallery to find the best match.…”

Section: Introductionmentioning

confidence: 99%

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Periocular region-based person identification in the visible, infrared and hyperspectral imagery

et al. 2015

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Face recognition performance degrades significantly under occlusions that occur intentionally or unintentionally due to head gear or hair style. In many incidents captured by surveillance videos, the offenders cover their faces leaving only the periocular region visible. We present an extensive study on periocular region based person identification in video. While, previous techniques have handpicked a single best frame from videos, we formulate, for the first time, periocular region based person identification in video as an image-set classification problem. For thorough analysis, we perform experiments on periocular regions extracted automatically from RGB videos, NIR videos and hyperspectral image cubes. Each image-set is represented by four heterogeneous feature types and classified with six state-of-the-art image-set classification algorithms. We propose a novel two stage inverse Error Weighted Fusion algorithm for feature and classifier score fusion. The proposed two stage fusion is superior to single stage fusion. Comprehensive experiments were performed on four standard datasets, MBGC NIR and visible spectrum [1], CMU Hyperspectral [2] and UBIPr [3]. We obtained average rank-1 recognition rates of 99.8, 98.5, 97.2, and 99.5% respectively which are significantly higher than the existing state of the art. Our results demonstrate the feasibility of image-set based periocular biometrics for real world applications.

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“…Periocular region includes the iris [10], eyes, eyelids, eye lashes, and part of the eyebrows [13]. Recognition using periocular biometrics is an emerging research area.…”

Section: Survey Of Periocular Biometrics Researchmentioning

confidence: 99%

Section: Survey Of Periocular Biometrics Researchmentioning

confidence: 99%

Section: Automatic Periocular Region Detection In Videosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Periocular region-based person identification in the visible, infrared and hyperspectral imagery

et al. 2015

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“…All the results indicate that the eye region exhibit the highest discriminative value. Other works presenting results for particular regions of the face can be found in (Savvides et al 2004a(Savvides et al , b, 2006Neo et al 2007Neo et al , 2010Teo et al 2007;Wright et al 2009;Woodard et al 2010;Park et al 2011).…”

Section: Introductionmentioning

confidence: 99%

A study in facial regions saliency: a fuzzy measure approach

et al. 2013

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People recognize familiar faces in a similar way by using interior facial features (facial regions) such as eyes, nose, mouth, etc. However, the importance of these regions in the realization of face identification and a quantification of the impact of such regions on the recognition process could vary from one region to another. An intuitively appealing observation is that of monotonicity: the more regions are taken into account in the recognition process, the better. From a formal point of view, the relevance of the facial regions and an aggregation of these pieces of experimental evidence can be described in the formal setting of fuzzy measures. Fuzzy measures are of particular interest with this regard given their monotonicity property (which stands in a clear contrast with the more restrictive additivity property inherent to probability-like measures). In this study, we concentrate on the construction of fuzzy measures (more specifically, k-fuzzy measure) and characterize their performance in the problem of face recognition using a collection of experimental data.

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Biometric Identification Based on Forearm Vein Pattern

Choraś

2019

VipIMAGE 2019

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Periocular region appearance cues for biometric identification

Cited by 97 publications

References 15 publications

Periocular region-based person identification in the visible, infrared and hyperspectral imagery

Periocular region-based person identification in the visible, infrared and hyperspectral imagery

A study in facial regions saliency: a fuzzy measure approach

Biometric Identification Based on Forearm Vein Pattern

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