Eye detection by complex filtering for periocular recognition

Abstract

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.

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

“…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.…”

“…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.…”

Exploting periocular and RGB information in fake iris detection

“…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).…”

“…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).…”

Fake Iris Detection: A Comparison between Near-Infrared and Visible Images

“…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).…”