Performance evaluation of local appearance based periocular recognition

Computer Vision – ECCV 2012. Workshops and Demonstrations

2012

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.

Section: Introduction and Related Workmentioning

confidence: 76%

Section: Introduction and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Periocular Recognition Using Retinotopic Sampling and Gabor Decomposition

Computer Vision – ECCV 2012. Workshops and Demonstrations

2012

“…Also, the periocular region appears in iris images, so fusion with the iris texture has potential to improve the overall recognition [5]. Periocular region has also shown superior performance than face under extreme values of blur or down-sampling [6]. This points out the strength of periocular recognition when only partial face images are available, for example forensics or surveillance, or in more relaxed scenarios such as distant acquisition or mobile devices.…”

Section: Introductionmentioning

confidence: 93%

Eye detection by complex filtering for periocular recognition

2nd International Workshop on Biometrics and Forensics

2014

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.

“…Most face systems use a holistic approach, requiring a full face image, so occlusion affects performance dramatically [2]. Periocular region has also shown superior performance than face under extreme blur or down-sampling [3]. In addition, the periocular region appears in iris images, so fusion with the iris texture has potential to improve the overall recognition [4].…”

Section: Introductionmentioning

confidence: 99%

Best regions for periocular recognition with NIR and visible images

2014 IEEE International Conference on Image Processing (ICIP)

2014

We evaluate the most useful regions for periocular recognition. For this purpose, we employ our periocular algorithm based on retinotopic sampling grids and Gabor analysis of the spectrum. We use both NIR and visible iris images. The best regions are selected via Sequential Forward Floating Selection (SFFS). The iris neighborhood (including sclera and eyelashes) is found as the best region with NIR data, while the surrounding skin texture (which is over-illuminated in NIR images) is the most discriminative region in visible range. To the best of our knowledge, only one work in the literature has evaluated the influence of different regions in the performance of periocular recognition algorithms. Our results are in the same line, despite the use of completely different matchers. We also evaluate an iris texture matcher, providing fusion results with our periocular system as well.