Merge pull request #4 from worldcoin/yi-update_performance_card

Yi update performance card

IRIS_PERFORMANCE_CARD.md

@@ -2,13 +2,13 @@
 
 ## ICE 2005 dataset
 
-ICE 2005 dataset is used by the National Institute of Standards and Technology (NIST) in Iris Challenge Evaluation (ICE) 2005 [1] and was made publicly available as part of the ND-Iris-0405 dataset from the University of Notre Dame du Lac [2]. This dataset was collected from 132 subjects using an LG EOU 2200 scanner with image resolution 640x480.
+ICE 2005 dataset is used by the National Institute of Standards and Technology (NIST) in Iris Challenge Evaluation (ICE) 2005 [1] and was made publicly available as part of the ND-Iris-0405 dataset from the University of Notre Dame [2]. This dataset was collected from 132 subjects using an LG EOU 2200 scanner with image resolution 640x480.
 
-## Comparison with ICE 2005 participants (commercial and academia)
+## Comparison with ICE 2005 participants
 
 A list of public and private entities participated in the ICE 2005 is shown below. The full evaluation report can be found in [1].
 
-<img src="./docs/performance_card/ice2005_results.png" alt="ICE 2005 results">
+<img src="./docs/performance_card/ice2005_results.png" width="800" alt="ICE 2005 results">
 
 Fig. 1(a) and 1(b) show the ROC on right and left eye, respectively, from all participants in Iris Challenge Evaluation (ICE) 2005 and Fig. 2(a) and 2(b) show our ROC in comparison. Although the exact error rates were not published in [1], these plots show IRIS performs as good as if not better than commercial algorithms available at the time.
 
@@ -47,12 +47,12 @@ The WLD-InHouse-v2 dataset was internally collected to simulate more challenging
 
 > **Note**: IRIS* is an optimised version of IRIS to accommodate high-resolution images captured by the Orb.
 
-Because of the high quality image capture enabled by the Orb and the effectiveness of iris encoding enabled by IRIS*, we are able to achieve high accuracy with FNMR less than 0.0001 at FMR of 0.000001 in WLD-InHouse-v2 dataset.
+Note that in the above public dataset tests, errors are mainly caused by poor image quality due to offgaze, motion blur and occlusion. These errors are greatly reduced in our WLD-InHouse-v2 dataset due to the auto-focus capability and stringent image quality control of the Orb. Together with the effectiveness of iris encoding enabled by IRIS*, we are able to achieve high accuracy with FNMR less than 0.0001 at FMR of 0.000001 in WLD-InHouse-v2 dataset.
 
 ## References
 
 1. Phillips, P. , Bowyer, K. , Flynn, P. , Liu, X. and Scruggs, W. (2008), The Iris Challenge Evaluation 2005, IEEE Second International Conference on Biometrics: Theory, Applications and Systems (BTAS 08), Arlington, VA, (https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=890057)
 2. Bowyer, K. , Flynn, P. (2016), The ND-IRIS-0405 Iris Image Dataset, (https://arxiv.org/abs/1606.04853)
-3. Othman, N. , Dorizzi, B. , Garcia-Salicetti, S. (2016), OSIRIS: An open source iris recognition software, Pattern Recognition Letters, vol.82, part2, pp.124-131, 2016. (https://github.com/tohki/iris-osiris)
+3. Othman, N. , Dorizzi, B. , Garcia-Salicetti, S. (2016), OSIRIS: An open source iris recognition software, Pattern Recognition Letters, vol.82, part2, pp.124-131, 2016. (https://github.com/tohki/iris-osiris)
 4. The ND-CrossSensor-Iris-2013 Dataset (Accessed 2018), (https://cvrl.nd.edu/projects/data/#nd-crosssensor-iris-2013-data-set)
 5. The Worldcoin Foundation blog (2023), Opening The Orb: A look inside Worldcoin’s biometric imaging device, (https://worldcoin.org/blog/engineering/opening-orb-look-inside-worldcoin-biometric-imaging-device)