Quality fusion based multimodal eye recognition

Abstract

Multimodal eye recognition can improve the biometric systems recognition accuracy by combining iris and sclera recognition. However, poor quality images can significantly affect the system performance. In this paper, we proposed a quality fusion based multimodal eye recognition. Our quality measure evaluated the entire eye image quality, iris area quality, and sclera area quality. The experimental results show that our overall iris and sclera quality scores are highly correlated to recognition accuracy, and our quality fusion based eye recognition can improve and predict the performance of eye recognition systems.

A new efficient and adaptive sclera recognition system

“…The table VI reflects a state-of-the-art comparative analysis of the most similar work on UBIRIS version 1 dataset. From the table it can be reflected that the proposed system produces a better result than the method proposed by Zhou et al in [5], and in [5] authors have also used manual sclera segmentation for few images, as their automatic segmentation failed on those occasion, and the very poor quality (e.g., blur, blink, or no sclera-area image) images were also discarded form the experimentation.…”

“…Here a score fusion based technique was adapted to combine the sclera and the iris feature. Further in [5] a quality fusion technique was used to combine sclera and iris feature and in [10] feature level combination was used to establish sclera and iris based multimodal biometrics. A survey on sclera recognition is recorded in [17].…”

“…First automatic segmentation processes for sclera biometric was proposed in [4], Otsu's algorithm was used to segment the sclera from a gray image. Sclera segmentation based on color image was first proposed in [5], HSV model was used to segment sclera from a color eye image and a bank of Gabor filter was used to enhance the sclera.…”

Sclera Recognition - A Survey

“…In order to increase the robustness of the sclera literature a quality fusion-based biometric technique using sclera and iris information was explained in [27]. Here a quality function was defined for both iris and sclera in order to combine them.…”

“…Various performances on UBIRIS Version 1 are listed in table II. 3.02 [39] FAR=0 and FRR= 0.0769 for the images of poor quality [2] 11.89 [27] 91.42 [37] 0.47…”