![Flowchart of SOINN, shows the basic overflow of the proposed algorithm. This figure has been taken from [75].](https://www.researchgate.net/profile/Abhijit-Das-3/publication/300334032/figure/fig3/AS:631566497751050@1527588595575/Flowchart-of-SOINN-shows-the-basic-overflow-of-the-proposed-algorithm-This-figure-has_Q320.jpg)
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An Online Learning-Based Adaptive Biometric System
Abstract and Figures
In the last decade, adaptive biometrics has become an emerging field of research. Considering the fact that limited work has been undertaken on adaptive biometrics using machine learning techniques, in this chapter we list and discuss a few out of many potential learning techniques that can be applied to build an adaptive biometric system. In order to illustrate the efficacy of one of the incremental learning techniques from the literature, we built an adaptive biometric system. For experimentation, we have used multi-modal ocular (sclera and iris) data. The preliminary results have been reported in the results section, which are very promising.
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... An increasing amount of research directed towards exploring sclera as a biometric trait has been presented in the literature in the last few years. This includes techniques for recognition [4,5,6,7,8,3], segmentation [2,9,10], presentation attacks detection [2,3,6], adaptability of the trait [11,12], information fusion with the iris [13,14] and synthetic sclera generation [15]. However, despite this research effort, comprehensive studies investigating the characteristics of sclera biometrics in mobile scenarios are still limited in the literature. ...
The paper presents a summary of the 2020 Sclera Seg-mentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 groups took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting.
... The systems have not achieved higher performance with respect to the other algorithms proposed in SSRBC 1 and the work of [7] have achieved better performance. Perhaps cutting age featuring [12,13] and classification method are required investigating this subject of research to attend better recognition performance. ...
... Sclera is the white region containing blood vessel patterns around the eyeball. Recently, as with other ocular biometric traits, sclera biometrics has increased in popularity [1][2][3][4][5][6][7][8][9][10][11]. Some recent investigations performed on multi-modal eye recognition (using iris and sclera) show that iris information fusion with sclera can enhance the applicability of iris biometrics in off-angle or off-axis eye gaze. ...
This work proposes a sclera vessel texture pattern synthesis technique. Sclera texture was synthesized by a non-parametric based texture regeneration technique. A small number of classes from the UBIRIS version: 1 dataset was employed as primitive images. An appreciable result was achieved which solicits the successful synthesis of sclera texture patterns. It is difficult to get a huge collection real sclera data and hence such synthetic data will be useful to the researchers.
... Sclera is the white region with blood vessel patterns around the eyeball. Recently, as with other ocular biometric traits, sclera biometrics has gained in popularity [1][2][3][4][5][6][7][8][9][10][11]. Some recent investigations performed on multi-modal eye recognition (using iris and sclera) show that iris information fusion with sclera can enhance the biometric applicability of iris biometrics in off-angle or off-axis eye gaze. ...
This work proposes a sclera vessel texture pattern synthesis technique. Sclera texture was synthesized by a non-parametric based texture regeneration technique. A small number of classes from the UBIRIS version: 1 dataset was employed as primitive images. An appreciable result was achieved which solicits the successful synthesis of sclera texture patterns.. It is difficult to get a huge collection real sclera data and hence such sysnthetic data will be useful to the researchers.
... 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. ...
This paper proposes a sclera vessel recognition technique. The vessel patterns of sclera are unique for each individual, which poses a degree of randomness and this can be utilized to identify a person uniquely. In this paper we propose a model-based sclera segmentation algorithm based on the K-means algorithm. As the sclera vessels are not prominent in the images, their contrast is enhanced using a Haar high-pass filter after equalization. A SIFT algorithm is used as a classifier for user recognition. The experiments are performed using different schemes and techniques that also include iris and sclera fusion. A subset of the UBIRIS version 1database is employed for experimentation which reflects encouraging results.
The proceedings of the 2001 Neural Information Processing Systems (NIPS) Conference.
The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2001 conference.
Bradford Books imprint
An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the Kuhn-Tucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is reversible, and decrementai "unlearning" offers an efficient method to exactly evaluate leave-one-out generalization performance. Interpretation of decrementai unlearning in feature space sheds light on the relationship between generalization and geometry of the data.
