Eye detection by complex filtering for periocular recognition

Alonso‐Fernandez, Fernando; Bigün, Josef

doi:10.1109/iwbf.2014.6914250

Cited by 14 publications

(32 citation statements)

References 25 publications

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“…Concerning the iris matchers, their performance is, in general, much better than the periocular matchers with NIR databases. This is expected, since iris systems usually work better in NIR range [6], and it confirms other studies using only BIOSEC and MobBIO databases [7]. On the other hand, the periocular matchers perform better than the iris matchers with VW data.…”

Section: Results: Individual Modalitiessupporting

confidence: 87%

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Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images

Alonso‐Fernandez

Mikaelyan

Bigün

2015

3rd International Workshop on Biometrics and Forensics (IWBF 2015)

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Periocular refers to the facial region in the eye vicinity. It can be easily obtained with existing face and iris setups, and it appears in iris images, so its fusion with the iris texture has a potential to improve the overall recognition. It is also suggested that iris is more suited to near-infrared (NIR) illumination, whereas the periocular modality is best for visible (VW) illumination. Here, we evaluate three periocular and three iris matchers based on different features. As experimental data, we use five databases, three acquired with a close-up NIR camera, and two in VW light with a webcam and a digital camera. We observe that the iris matchers perform better than the periocular matchers with NIR data, and the opposite with VW data. However, in both cases, their fusion can provide additional performance improvements. This is specially relevant with VW data, where the iris matchers perform significantly worse (due to low resolution), but they are still able to complement the periocular modality.

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Section: Results: Individual Modalitiessupporting

confidence: 87%

“…Also, Alonso-Fernandez and Bigun. [7] fused iris and periocular modalities using close-up NIR camera and VW webcam data. With NIR data, the iris matcher performed much better, and the fusion did not improve performance.…”

Section: Introductionmentioning

confidence: 99%

Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images

Alonso‐Fernandez

Mikaelyan

Bigün

2015

3rd International Workshop on Biometrics and Forensics (IWBF 2015)

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“…We also conduct detection experiments to localize the eye center position, which is used as input of the GLCM feature extraction algorithm, so as to extract GLCM features in the relevant eye/periocular region only. For this purpose, we employ our eye detection algorithm based on symmetry filters [23]. A circular mask of radius R=60 pixels is placed in the eye center, masking the corresponding outer (periocular) or inner (eye) region, depending on the experiment at hand (see Figure 3).…”

Section: Database and Protocolmentioning

confidence: 99%

“…A circular mask of radius R=60 pixels is placed in the eye center, masking the corresponding outer (periocular) or inner (eye) region, depending on the experiment at hand (see Figure 3). The radius has been chosen empirically, based on the maximum radius of the outer (sclera) iris circle obtained by ground-truth annotation of the MobBIO database [23]. Figure 4 shows the distribution of GLCM features on the real and fake iris images of the database (averaged between all images of each set, and normalized to the [0,1] range).…”

Section: Database and Protocolmentioning

confidence: 99%

Exploting periocular and RGB information in fake iris detection

Alonso‐Fernandez

Bigün

2014

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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Abstract-Fake iris detection has been studied by several researchers. However, to date, the experimental setup has been limited to near-infrared (NIR) sensors, which provide grey-scale images. This work makes use of images captured in visible range with color (RGB) information. We employ Gray-Level CoOccurrence textural features and SVM classifiers for the task of fake iris detection. The best features are selected with the Sequential Forward Floating Selection (SFFS) algorithm. To the best of our knowledge, this is the first work evaluating spoofing attack using color iris images in visible range. Our results demonstrate that the use of features from the three color channels clearly outperform the accuracy obtained from the luminance (gray scale) image. Also, the R channel is found to be the best individual channel. Lastly, we analyze the effect of extracting features from selected (eye or periocular) regions only. The best performance is obtained when GLCM features are extracted from the whole image, highlighting that both the iris and the surrounding periocular region are relevant for fake iris detection. An added advantage is that no accurate iris segmentation is needed. This work is relevant due to the increasing prevalence of more relaxed scenarios where iris acquisition using NIR light is unfeasible (e.g. distant acquisition or mobile devices), which are putting high pressure in the development of algorithms capable of working with visible light.

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“…We employ our eye detection algorithm based on symmetry filters described in [25]. A circular mask of fixed radius is placed in the eye center, masking the corresponding outer (periocular) or inner (eye) region, depending on the experiment at hand (Figure 3).…”

Section: Database and Protocolmentioning

confidence: 99%