A new efficient and adaptive sclera recognition system

Abstract

In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional Pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features; further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation.

Fast and efficent multimodal eye biometrics using projective dictionary pair learning

“…In [29,30] the research uses a multisession experiment scenario and the authors have also considered all images, but in contrast to our work, they have achieved a lower recognition accuracy.…”

“…With regards to the time complexity of the algorithm, our technique outperforms the systems proposed in [22,25,27], as they used template matching techniques. Whereas in [28,29,30] dense patch-based features are used coupled with SPM and SVM and hence takes more than 2 seconds for classification of a query image. Whereas, our proposed algorithm could perform in ~ 1 second, preserving better accuracy.…”

“…It is evident from the literature that most of the individual work on the sclera [23,27] and multimodal eye recognition techniques [22,24,25] using the sclera and the iris employs a template matching-based technique for pattern classification, which is quite time consuming. Although in subsequent work of sclera biometrics [28][29][30][31][32] sophisticated classifier techniques such as Support Vector Machines are employed, however the time complexity of the feature extraction process is quite high.…”

Survey Paper on Sclera Segmentations Techniques

“…A discrete Meyer wavelet filter banks and Local Directional Pattern (LDP) were used in [16] for blood vessel enhancement and feature extraction. Finally, Alkassar et al [17] proposed a new sclera segmentation and occluded eye detection for sclera validation.…”

“…iris centre location is known. In 2014, Abhijit et al proposed a method for sclera segmentation based on Fuzzy logic [23].…”