Ramachandra Raghavendra

Smartphone based visible iris recognition using deep sparse filtering

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.

Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes. HighlightsNovel adaptive weighted image fusion scheme to combine different multi-spectral palmprints.Dynamic switching between selection and weighted fusion to achieve high accuracy.Novel Scheme for palmprint ROI extraction based on hand shape.Extensive qualitative and quantitative analysis of the proposed (ROI & fusion) scheme.Benchmarking with well known state-of-the-art schemes that include 5 different contemporary multi-spectral image fusion schemes and 2 well known state-of-the-art palmprint recognition schemes.

Presentation Attack Detection for Face Recognition Using Light Field Camera

The vulnerability of face recognition systems is a growing concern that has drawn the interest from both academic and research communities. Despite the availability of a broad range of face presentation attack detection (PAD) (or countermeasure or antispoofing) schemes, there exists no superior PAD technique due to evolution of sophisticated presentation attacks (or spoof attacks). In this paper, we present a new perspective for face presentation attack detection by introducing light field camera (LFC). Since the use of a LFC can record the direction of each incoming ray in addition to the intensity, it exhibits an unique characteristic of rendering multiple depth (or focus) images in a single capture. Thus, we present a novel approach that involves exploring the variation of the focus between multiple depth (or focus) images rendered by the LFC that in turn can be used to reveal the presentation attacks. To this extent, we first collect a new face artefact database using LFC that comprises of 80 subjects. Face artefacts are generated by simulating two widely used attacks, such as photo print and electronic screen attack. Extensive experiments carried out on the light field face artefact database have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well established state-of-the-art schemes.

Robust Scheme for Iris Presentation Attack Detection Using Multiscale Binarized Statistical Image Features

Vulnerability of iris recognition systems remains a challenge due to diverse presentation attacks that fail to assure the reliability when adopting these systems in real-life scenarios. In this paper, we present an in-depth analysis of presentation attacks on iris recognition systems especially focusing on the photo print attacks and the electronic display (or screen) attack. To this extent, we introduce a new relatively large scale visible spectrum iris artefact database comprised of 3300 iris normal and artefact samples that are captured by simulating five different attacks on iris recognition system. We also propose a novel presentation attack detection (PAD) scheme based on multiscale binarized statistical image features and linear support vector machines. Extensive experiments are carried out on four different publicly available iris artefact databases that have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well-established state-of-the-art schemes.

A new perspective — Face recognition with light-field camera

Face recognition has received a substantial attention from industry and also academics. The improvement in the image sensors has further boosted the performance of the face recognition algorithm in real-world scenarios. In this paper, we evaluate the strength of the light-field camera for for face recognition applications. The main advantage of a light-field camera is that, it can provide different focus (or depth) images in a single capture which is not possible with a conventional 2D camera. We first collected a new face dataset using both the light-field and a conventional camera by simulating real-world scenarios. We then propose a new scheme to select the best focused face image from a set of focus images rendered by the light-field camera. Extensive experiments are carried out on our new face dataset to bring out the merits and demerits of employing the light-field camera for face recognition applications.

Combining Iris and Periocular Recognition Using Light Field Camera

Iris and Periocular biometrics has proved its effectiveness in accurately verifying the subject of interest. Recent improvements in visible spectrum Iris and Periocular verification have further boosted its application to unconstrained scenarios. However existing visible Iris verification systems suffer from low quality samples because of the limited depth-of-field exhibited by the conventional Iris capture systems. In this work, we propose a robust Iris and Periocular erification scheme in visible spectrum using Light Field Camera (LFC). Since the light field camera can provide multiple focus images in single capture, we are motivated to investigate its applicability for robust Iris and Periocular verification by exploring its all-in-focus property. Further, the use of all-in-focus property will extend the depth-of-focus and overcome the problem of focus that plays a predominant role in robust Iris and Periocular verification. We first collect a new Iris and Periocular biometric database using both light field and conventional camera by simulating real life scenarios. We then propose a new scheme for feature extraction and classification by exploring the combination of Local Binary Patterns (LBP) and Sparse Reconstruction Classifier (SRC). Extensive experiments are carried out on the newly collected database to bring out the merits and demerits on applicability of light field camera for Iris and Periocular verification. Finally, we also present the results on combining the information from Iris and Periocular biometrics using weighted sum rule.

Multi-modal authentication system for smartphones using face, iris and periocular

Secure authentication for smartphones is becoming important for many applications such as financial transactions. Until today PIN and password authentication are the most commonly used methods for smartphone access control. Specifically for a PIN and limited length passwords, the level of security is low and thus can be compromised easily. In this work, we propose a multi-modal biometric system, which uses face, periocular and iris biometric characteristics for authentication. The proposed system is tested on two different devices - Samsung Galaxy S5 smartphone and Samsung Galaxy Note 10.1 tablet. An extensive set of experiments conducted using the proposed system shows the applicability for secure authentication scenarios. The proposed system is tested using uni-modal and multi-modal approach. An Equal Error Rate (EER) of 0.68% is obtained from the experiments validating the robust performance of the proposed system.

Scaling-robust fingerprint verification with smartphone camera in real-life scenarios

We propose a new scheme for accurate contactless fingerprint recognition captured with smartphone cameras under various real-life scenarios. The proposed scheme can be structured using three building blocks namely: (1) finger segmentation (2) pre-processing and scaling (3) minutiae extraction and comparison. The proposed finger segmentation scheme is based on Mean Shift Segmentation (MSS) algorithm followed by multiple metrics to accurately segment the finger from the background. We then propose a new scheme to perform the finger scaling to accurately extract the fingerprint region from the segmented finger. Finally, the comparison is carried out based on the minutiae features extracted from the scaled fingerprint images. Extensive experiments are carried out on our recently collected contactless fingerprint dataset consisting of 1800 samples from 25 subjects. In order to effectively evaluate the robustness of the proposed scheme, the whole dataset is constructed using three different smartphones namely: Nokia N8, iPhone 4 and Samsung S1. The experimental results have shown the effectiveness of the proposed scheme on various complex backgrounds with an Equal Error Rate of 3.74% noted on Samsung S1 smartphone camera.

A low-cost multimodal biometric sensor to capture finger vein and fingerprint

Multimodal biometric systems based on fingerprint and finger vein modality provide promising features useful for robust and reliable identity verification. In this paper, we present a robust imaging device that can capture both fingerprint and finger vein simultaneously. The presented low-cost sensor employs a single camera followed by both near infrared and visible light sources organized along with the physical structures to capture good quality finger vein and fingerprint samples. We further present a novel finger vein recognition algorithm that explores both the maximum curvature method and Spectral Minutiae Representation (SMR). Extensive experiments are carried out on our newly collected database that comprises of 1500 samples of fingerprint and finger vein from 150 unique fingers corresponding to 41 subjects. Our results demonstrate the efficacy of the proposed sensor with a lowest Equal Error Rate of 0.78%.

Robust iris recognition using light-field camera

Iris is one of the preferred biometric modalities. Nevertheless, the focus of iris image has to be good enough to achieve good recognition performance. Traditional iris imaging devices in the visible spectrum suffer from limited depth-of-field which results in out-of-focus iris images. The acquisition of iris image is thus repeated until a satisfactory focus is obtained or the image is post-processed to improve the visibility of texture pattern. Bad focused images obtained due to non-optimal focus degrade the identification rate. In this work, we propose a novel scheme to capture high quality iris samples by exploring new sensors based on light-field technology to address the limited depth-of-field exhibited by the conventional iris sensors. The idea stems out from the availability of multiple depth/focus images in a single exposure. We propose to use the best-focused iris image from the set of depth images rendered by the Light-field Camera (LFC). We further evaluate the proposed scheme experimentally with a unique and newly acquired iris database simulating the real-life scenario.