Periocular Biometrics in the Visible Spectrum

Park, Unsang; Jillela, Raghavender; Ross, Arun; Jain, Anil K.

doi:10.1109/tifs.2010.2096810

Cited by 265 publications

(183 citation statements)

References 27 publications

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

Section: Introductionmentioning

confidence: 99%

Eye detection by complex filtering for periocular recognition

Alonso‐Fernandez

Bigün

2014

2nd International Workshop on Biometrics and Forensics

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We present a novel system to localize the eye position based on symmetry filters. By using a 2D separable filter tuned to detect circular symmetries, detection is done with a few 1D convolutions. The detected eye center is used as input to our periocular algorithm based on retinotopic sampling grids and Gabor analysis of the local power spectrum. This setup is evaluated with two databases of iris data, one acquired with a close-up NIR camera, and another in visible light with a webcam. The periocular system shows high resilience to inaccuracies in the position of the detected eye center. The density of the sampling grid can also be reduced without sacrificing too much accuracy, allowing additional computational savings. We also evaluate an iris texture matcher based on 1D Log-Gabor wavelets. Despite the poorer performance of the iris matcher with the webcam database, its fusion with the periocular system results in improved performance.

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Section: Introductionmentioning

confidence: 99%

Eye detection by complex filtering for periocular recognition

Alonso‐Fernandez

Bigün

2014

2nd International Workshop on Biometrics and Forensics

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“…Recognition using periocular biometrics is an emerging research area. Initial feasibility studies were done by Park et al [9,10] who detected the iris in the visible spectrum images and extracted features at fixed grid positions around the center of the limbus circle. Experiments on a subset of the FRGC v2.0 [1] dataset showed that their algorithm's performance depends on the accurate iris detection and is sensitive to the eyeball movements.…”

Section: Survey Of Periocular Biometrics Researchmentioning

confidence: 99%

“…Periocular biometrics can be acquired from a larger distance by surveillance cameras whereas iris scanning requires the subjects to open their eyes fully and look into a scanner from a very close range. The latter may not be socially acceptable to users [9,10]. In many practical scenarios, the subject may not be cooperative or willing, or even aware that he/she is being imaged.…”

Section: Introductionmentioning

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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“…Such a system would receive as input a specific sub-region of the face containing components such as a nose, mouth, or eyebrows. However, with the exception of perioccular recognition systems [10], few advances have been made in component based recognition.…”

Section: B Component-based Recognitionmentioning

confidence: 99%

Face recognition: Some challenges in forensics

Jain

Klare

Park

2011

Face and Gesture 2011

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Abstract-Face recognition has become a valuable and routine forensic tool used by criminal investigators. Compared to automated face recognition, forensic face recognition is more demanding because it must be able to handle facial images captured under non-ideal conditions and it has high liability for following legal procedures. This paper discusses recent developments in automated face recognition that impact the forensic face recognition community. Improvements in forensic face recognition through research in facial aging, facial marks, forensic sketch recognition, face recognition in video, nearinfrared face recognition, and use of soft biometrics will be discussed. Finally, current limitations and future research directions for face recognition in forensics are suggested.

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Periocular Biometrics in the Visible Spectrum

Cited by 265 publications

References 27 publications

Eye detection by complex filtering for periocular recognition

Eye detection by complex filtering for periocular recognition

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

Face recognition: Some challenges in forensics

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