Vinay Krishna Vemuri
Other affiliations: Gjøvik University College
Bio: Vinay Krishna Vemuri is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Iris recognition & Biometrics. The author has an hindex of 2, co-authored 2 publications receiving 171 citations. Previous affiliations of Vinay Krishna Vemuri include Gjøvik University College.
TL;DR: A new segmentation scheme is proposed and adapted to smartphone based visible iris images for approximating the radius of the iris to achieve robust segmentation and a new feature extraction method based on deepsparsefiltering is proposed to obtain robust features for unconstrained iris image images.
Abstract: Good biometric performance of iris recognition motivates it to be used for many large scale security and access control applications. Recent works have identified visible spectrum iris recognition as a viable option with considerable performance. Key advantages of visible spectrum iris recognition include the possibility of iris imaging in on-the-move and at-a-distance scenarios as compared to fixed range imaging in near-infra-red light. The unconstrained iris imaging captures the images with largely varying radius of iris and pupil. In this work, we propose a new segmentation scheme and adapt it to smartphone based visible iris images for approximating the radius of the iris to achieve robust segmentation. The proposed technique has shown the improved segmentation accuracy up to 85% with standard OSIRIS v4.1. This work also proposes a new feature extraction method based on deepsparsefiltering to obtain robust features for unconstrained iris images. To evaluate the proposed segmentation scheme and feature extraction scheme, we employ a publicly available database and also compose a new iris image database. The newly composed iris image database (VSSIRIS) is acquired using two different smartphones - iPhone 5S and Nokia Lumia 1020 under mixed illumination with unconstrained conditions in visible spectrum. The biometric performance is benchmarked based on the equal error rate (EER) obtained from various state-of-art schemes and proposed feature extraction scheme. An impressive EER of 1.62% is obtained on our VSSIRIS database and an average gain of around 2% in EER is obtained on the public database as compared to the well-known state-of-art schemes.
••13 Jun 2016
TL;DR: A preliminary study on 84 data subjects is presented to reveal the effect of cataract on iris recognition performance and results obtained indicate the degraded performance in verification of theCataract operated eye.
Abstract: Iris biometrics is considered as the unique and accurate biometric characteristics that are suited for large scale applications such as India's AADHAR, CANPASS, and many other national ID programs. However, the accuracy of the iris recognition is observed to degrade when eye (or iris) is affected by the diseases. Among many other eye diseases, cataract results in a cloud formation on the eye lens and is a potential problem, especially in the developing countries. In this paper, we present a preliminary study on 84 data subjects to reveal the effect of cataract on iris recognition performance. We investigate three different scenarios, which involve: (1) Enrolment and recognition of affected eyes (pre-operated eye) (2) Enrolement and recognition of operated eyes (post-operated eye) (3) Enrolment with affected eyes and recognition with operated eyes. Extensive experiments are carried out using five different academic and commercial iris recognition methods to get complete insight on the impact of cataract on the recognition performance. Results obtained on our database indicate the degraded performance in verification of the cataract operated eye.
TL;DR: A two-stage learning method inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data for intelligent diagnosis of machines that reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.
Abstract: Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are time-consuming and labor-intensive. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed for intelligent diagnosis of machines. In the first learning stage of the method, sparse filtering, an unsupervised two-layer neural network, is used to directly learn features from mechanical vibration signals. In the second stage, softmax regression is employed to classify the health conditions based on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed method obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.
TL;DR: This article surveys 100 different approaches that explore deep learning for recognizing individuals using various biometric modalities and discusses how deep learning methods can benefit the field of biometrics and the potential gaps that deep learning approaches need to address for real-world biometric applications.
Abstract: In the recent past, deep learning methods have demonstrated remarkable success for supervised learning tasks in multiple domains including computer vision, natural language processing, and speech processing. In this article, we investigate the impact of deep learning in the field of biometrics, given its success in other domains. Since biometrics deals with identifying people by using their characteristics, it primarily involves supervised learning and can leverage the success of deep learning in other related domains. In this article, we survey 100 different approaches that explore deep learning for recognizing individuals using various biometric modalities. We find that most deep learning research in biometrics has been focused on face and speaker recognition. Based on inferences from these approaches, we discuss how deep learning methods can benefit the field of biometrics and the potential gaps that deep learning approaches need to address for real-world biometric applications.
01 Sep 2016
TL;DR: Experimental analysis reveal that proposed DeepIrisNet can model the micro-structures of iris very effectively and provides robust, discriminative, compact, and very easy-to-implement iris representation that obtains state-of-the-art accuracy.
Abstract: Despite significant advances in iris recognition (IR), the efficient and robust IR at scale and in non-ideal conditions presents serious performance issues and is still ongoing research topic. Deep Convolution Neural Networks (DCNN) are powerful visual models that have reported state-of-the-art performance in several domains. In this paper, we propose deep learning based method termed as DeepIrisNet for iris representation. The proposed approach bases on very deep architecture and various tricks from recent successful CNNs. Experimental analysis reveal that proposed DeepIrisNet can model the micro-structures of iris very effectively and provides robust, discriminative, compact, and very easy-to-implement iris representation that obtains state-of-the-art accuracy. Furthermore, we evaluate our iris representation for cross-sensor IR. The experimental results demonstrate that DeepIrisNet models obtain a significant improvement in cross-sensor recognition accuracy too.
TL;DR: This survey aims to provide a more comprehensive introduction to Sensor-based human activity recognition (HAR) in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods.
Abstract: Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR.
TL;DR: A novel approach for iris normalization, based on a non geometric parameterization of contours is proposed in the latest version: OSIRISV4.1 and is detailed in particular here.
Abstract: In this paper, we present the evolution of the open source iris recognition system OSIRIS through its more relevant versions: OSIRISV2, OSIRISV4, and OSIRISV4.1. We developed OSIRIS in the framework of BioSecure Association as an open source software aiming at providing a reference for the scientific community. The software is mainly composed of four key modules, namely segmentation, normalization, feature extraction and template matching, which are described in detail for each version. A novel approach for iris normalization, based on a non geometric parameterization of contours is proposed in the latest version: OSIRISV4.1 and is detailed in particular here. Improvements in performance through the different versions of OSIRIS are reported on two public databases commonly used, ICE2005 and CASIA-IrisV4-Thousand. We note the high verification rates obtained by the last version. For this reason, OSIRISV4.1 can be proposed as a baseline system for comparison to other algorithms, this way supplying a helpful research tool for the iris recognition community.