Ioannis Rigas

Biometric identification based on the eye movements and graph matching techniques

The last few years a growing research interest has aroused in the field of biometrics, concerning the use of brain dependent characteristics generally known as behavioral features. Human eyes, often referred as the gates to the soul, can possibly comprise a rich source of idiosyncratic information which may be used for the recognition of an individuals identity. In this paper an innovative experiment and a novel processing approach for the human eye movements is implemented, ultimately aiming at the biometric segregation of individual persons. In our experiment, the subjects observe face images while their eye movements are being monitored, providing information about each participants attention spots. The implemented method treats eye trajectories as 2-D distributions of points on the image plane. The efficiency of graph objects in the representation of structural information motivated us on the utilization of a non-parametric multivariate graph-based measure for the comparison of eye movement signals, yielding promising results at the task of identification according to behavioral characteristics of an individual.

Human eye movements as a trait for biometrical identification

This research work proposes an innovative processing scheme for the exploitation of eye movement dynamics on the field of biometrical identification. As the mechanisms that derive eye movements highly depend on each persons idiosyncrasies, cues that reflect at a certain extent individual characteristics may be captured and subsequently deployed for the implementation of a robust identification system. Our methodology involves the employment of a non - parametric statistical test, the multivariate Wald - Wolfowitz test (WW-test), in order to compare the distributions of saccadic velocity and acceleration features, which are extracted while a person fixates on visual stimuli. In the evaluation section we use two publicly available datasets that supply recorded eye movements from a number of subjects during the observation of a moving spot on a computer screen. The resulting identification rates exhibit the efficacy of the suggested scheme to adequately segregate people according to their eye movement traits.

Biometric Recognition via Probabilistic Spatial Projection of Eye Movement Trajectories in Dynamic Visual Environments

This paper proposes a method for the extraction of biometric features from the spatial patterns formed by eye movements during an inspection of dynamic visual stimulus. In the suggested framework, each eye movement signal is transformed into a time-constrained decomposition by using a probabilistic representation of spatial and temporal features related to eye fixations and called fixation density map (FDM). The results for a large collection of eye movements recorded from 200 individuals indicate the best equal error rate of 10.8% and Rank-1 identification rate as high as 51%, which is a significant improvement over existing eye movement-driven biometric methods. In addition, our experiments reveal that a person recognition approach based on the FDM performs well even in cases when eye movement data are captured at lower than optimum sampling frequencies. This property is very important for the future ocular biometric systems where existing iris recognition devices could be employed to combine eye movement traits with iris information for increased security and accuracy. Considering that commercial iris recognition devices are able to implement eye image sampling usually at a relatively low rate, the ability to perform eye movement-driven biometrics at such rates is of great significance.

Efficient modeling of visual saliency based on local sparse representation and the use of hamming distance

A method for the construction of saliency maps is proposed.Features are extracted via local sparse coding on image patches.The overcomplete dictionary is trained using natural images.A bio-plausible scheme based on the Hamming distance is used to compare patch representations.The algorithm is efficient both in terms of computational cost and of detection performance. Modeling of visual saliency is an important domain of research in computer vision, given the significant role of attention mechanisms during neural processing of visual information. This work presents a new approach for the construction of image representations of salient locations, generally known as saliency maps. The developed method is based on an efficient comparison scheme for the local sparse representations deriving from non-overlapping image patches. The sparse coding stage is implemented via an overcomplete dictionary trained with a soft-competitive bio-inspired algorithm and the use of natural images. The resulting local sparse codes are pairwise compared using the Hamming distance as a gauge of their co-activation. The calculated distances are used to quantify the saliency strength for each individual patch, and then, the saliency values are non-linearly filtered to form the final map. The evaluation results obtained on four image databases, demonstrate the competitive performance of the proposed approach compared to several state-of-the-art saliency modeling algorithms. More importantly, the proposed scheme is simple, efficient, and robust under a variety of visual conditions. Thus, it appears as an ideal solution for a hardware implementation of a frontend saliency modeling module in a computer vision system.

Low-Level Visual Saliency With Application on Aerial Imagery

In this letter, a method for the construction of low-level saliency maps is presented in tandem with their evaluation on a set of aerial images. One of the key inspirations for the current research lies on the observation that, usually, the most significant man-made structures in a wide-field aerial image resemble the low-level features that can be detected with a bottom-up saliency map. Aerial photography comprises, hence, a natural domain of application for a method that computationally models low-level saliency. With the employment of mechanisms analogous to the neural functions that drive human attention, we propose a bioinspired framework based on sparse coding for the extraction of information about saliency. The suggested algorithm is then evaluated on a novel data set that has been constructed with the utilization of aerial images and the corresponding manually designed ground truth binary maps of salient structures. The results demonstrate the efficiency of the proposed scheme to highlight conspicuous locations in aerial images, revealing the perspectives on the employment of low-level saliency maps in aerial imaging systems.

Towards a multi-source fusion approach for eye movement-driven recognition

We investigate the effects of multi-source fusion in the field of eye movement biometrics.We introduce the concept of multi-stimulus fusion.We suggest a weighted fusion scheme based on stimulus-specific and algorithm-specific weights.The proposed weight-training procedure is based on the identification performance.The results show a considerable improvement in performance for the field of eye movement biometrics. This paper presents a research for the use of multi-source information fusion in the field of eye movement biometrics. In the current state-of-the-art, there are different techniques developed to extract the physical and the behavioral biometric characteristics of the eye movements. In this work, we explore the effects from the multi-source fusion of the heterogeneous information extracted by different biometric algorithms under the presence of diverse visual stimuli. We propose a two-stage fusion approach with the employment of stimulus-specific and algorithm-specific weights for fusing the information from different matchers based on their identification efficacy. The experimental evaluation performed on a large database of 320 subjects reveals a considerable improvement in biometric recognition accuracy, with minimal equal error rate (EER) of 5.8%, and best case Rank-1 identification rate (Rank-1 IR) of 88.6%. It should be also emphasized that although the concept of multi-stimulus fusion is currently evaluated specifically for the eye movement biometrics, it can be adopted by other biometric modalities too, in cases when an exogenous stimulus affects the extraction of the biometric features.

Gaze estimation as a framework for iris liveness detection

This work investigates the possibility of detecting iris print-attacks via the analysis of a number of gaze-related features acquired in a process of eye tracking. Gaze estimation algorithms employ models based on the physical structure and function of the eye, providing thus a number of salient features that can be potentially employed for the detection of spoofing print-attacks. In our study, a combined dataset was assembled for the investigation of these features, consisting of eye movement recordings and the corresponding iris images collected from 100 subjects. The collected iris images were utilized in direct implementation of iris print-attacks against an eye tracking device. We developed a methodology for the detection of spoof indicative artifacts in the recorded signals, and fed the extracted features from the live and spoof eye signals into a two-class SVM classifier. The obtained results indicate a best correct classification rate (CCR) of 95.7%. Furthermore, we demonstrate the moderate decrease in liveness detection rates during subsampling of the eye movement signal to frequencies as low as 15 Hz. This result indicates the usefulness of running gaze estimation algorithms on existing iris recognition devices where such sampling frequency rate is common.

Biometric Recognition via Eye Movements: Saccadic Vigor and Acceleration Cues

Previous research shows that human eye movements can serve as a valuable source of information about the structural elements of the oculomotor system and they also can open a window to the neural functions and cognitive mechanisms related to visual attention and perception. The research field of eye movement-driven biometrics explores the extraction of individual-specific characteristics from eye movements and their employment for recognition purposes. In this work, we present a study for the incorporation of dynamic saccadic features into a model of eye movement-driven biometrics. We show that when these features are added to our previous biometric framework and tested on a large database of 322 subjects, the biometric accuracy presents a relative improvement in the range of 31.6--33.5% for the verification scenario, and in range of 22.3--53.1% for the identification scenario. More importantly, this improvement is demonstrated for different types of visual stimulus (random dot, text, video), indicating the enhanced robustness offered by the incorporation of saccadic vigor and acceleration cues.

Face recognition via local sparse coding

In this paper the face recognition problem is addressed in a part-based sparse approach through the comparison of respective facial regions between different images. To this purpose, a sparse coding procedure is applied to non-overlapping patches derived from frontal-face images, in order to extract local facial information. An adequate measure is introduced, incorporating the resulted sparse representation along with the Hamming distance, in order to express pairwise similarities between faces. Finally, a simple Nearest Neighbor classifier is employed to determine the identity of each facial image. In addition, a new criterion is presented for the rejection of outliers. The emerged face recognition scheme is evaluated using publicly available facial image databases, and the results are compared with those of other well-established recognition methods.

Current research in eye movement biometrics

On the onset of the second decade of research in eye movement biometrics, the already demonstrated results strongly support the promising perspectives of the field. This paper presents a description of the research conducted in eye movement biometrics based on an extended analysis of the characteristics and results of the BioEye 2015: Competition on Biometrics via Eye Movements. This extended presentation can contribute to the understanding of the current level of research in eye movement biometrics, covering areas such as the previous work in the field, the procedures for the creation of a database of eye movement recordings, and the different approaches that can be used for the analysis of eye movements. Also, the presented results from BioEye 2015 competition can demonstrate the potential identification accuracy that can be achieved under easier and more difficult scenarios. Based on the provided presentation, we discuss topics related to the current status in eye movement biometrics and suggest possible directions for the future research in the field. We present a review of the state-of-the-art in eye movement biometrics.We explain the general steps for the creation of a database of eye movement recordings.We describe basic eye movement features and methodologies with application in biometrics.We present extended analysis and results for the BioEye 2015 competition.