Personal identification using periocular skin texture

Miller, Philip E.; Rawls, Allen; Pundlik, Shrinivas; Woodard, Damon L.

doi:10.1145/1774088.1774408

Cited by 86 publications

(35 citation statements)

References 18 publications

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“…Periocular recognition has gained attention recently in the biometrics field [1][2][3][4][5][6][7][8][9] due to demands for increased robustness of face or iris systems. Periocular refers to the face region in the immediate vicinity of the eye, including the eye, eyelids, lashes and eyebrows.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Periocular Recognition Using Retinotopic Sampling and Gabor Decomposition

Alonso‐Fernandez

Bigün

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

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Abstract. We present a new system for biometric recognition using periocular images based on retinotopic sampling grids and Gabor analysis of the local power spectrum. A number of aspects are studied, including: 1) grid adaptation to dimensions of the target eye vs. grids of constant size, 2) comparison between circular-and rectangular-shaped grids, 3) use of Gabor magnitude vs. phase vectors for recognition, 4) rotation compensation between query and test images, and 5) comparison with an iris machine expert. Results show that our system achieves competitive verification rates compared with other periocular recognition approaches. We also show that top verification rates can be obtained without rotation compensation, thus allowing to remove this step for computational efficiency. Also, the performance is not affected substantially if we use a grid of fixed dimensions, or it is even better in certain situations, avoiding the need of accurate detection of the iris region.

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Section: Introduction and Related Workmentioning

confidence: 99%

Periocular Recognition Using Retinotopic Sampling and Gabor Decomposition

Alonso‐Fernandez

Bigün

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

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“…The use of Local Binary Patterns (LBP) [24] to represent the texture of the periocular region has been investigated by several researchers. Miller et al [25] used city block distance to classify LBP features. Their work was extended by Adams et al [15] using genetic algorithms to select the optimal subset of LBP features.…”

Section: Survey Of Periocular Biometrics Researchmentioning

confidence: 99%

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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“…Some researchers have investigated the use of Local Binary Pattern (LBP) features [17] to represent the texture of the periocular region for recognition purpose. Miller et al [13] used city block distance to classify LBP features of the periocular region. Their work was extended by Adams et al [2] using genetic algorithms to select the optimal subset of LBP features for the matching of periocular regions.…”

Section: Related Work On Periocular Biometricsmentioning

confidence: 99%

Periocular biometric recognition using image sets

Uzair

Mahmood

Mian

et al. 2013

2013 IEEE Workshop on Applications of Computer Vision (WACV)

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Human identification based on iris biometrics requires high resolution iris images of a cooperative subject. Such images cannot be obtained in non-intrusive applications such as surveillance. However, the full region around the eye, known as the periocular region, can be acquired nonintrusively and used as a biometric. In this paper we investigate the use of periocular region for person identification. Current techniques have focused on choosing a single best frame, mostly manually, for matching. In contrast, we formulate, for the first time, person identification based on periocular regions as an image set classification problem. We generate periocular region image sets from the Multi Biometric Grand Challenge (MBGC) NIR videos. Periocular regions of the right eyes are mirrored and combined with those of the left eyes to form an image set. Each image set contains periocular regions of a single subject. For imageset classification, we use six state-of-the-art techniques and report their comparative recognition and verification performances. Our results show that image sets of periocular regions achieve significantly higher recognition rates than currently reported in the literature for the same database.

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Personal identification using periocular skin texture

Cited by 86 publications

References 18 publications

Periocular Recognition Using Retinotopic Sampling and Gabor Decomposition

Periocular Recognition Using Retinotopic Sampling and Gabor Decomposition

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

Periocular biometric recognition using image sets

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