1,721,040 research outputs found
A biologically-inspired attentional approach for face recognition
No full textBiometric technologies are now widely used in personal mobile devices. The technological improvements in mobile computing platforms make it possible to embed resource-intensive processes such as human faces recognition. On the other hand, whenever the cooperation of the user is limited, such as in the case of continuous authentication, it is rather difficult to match human performances.
The neural architectures and Deep learning has shown great efficiency in the last decade and this mainly due to the computation between the image and the representation in the cortex area. In this paper we propose a biologically-inspired system to perform face recognition by processing image areas captured at different fixation points. The output of simple and complex cells in the V1 striate cortex is simulated by means of a simple convolutional network based on two kind of neurons: S1 and C1. The network layers implement Gabor filters and max pooling operations to encode facial features at different scales and orientations. Higher-level processes, related to face-selective areas, are reproduced through a classification layer. The main inconveniences of the DCNN is the requirement of a huge amount of data. In this paper we propose to add a preprocessing stage to process a small amount of data. The proposed system has been extensively tested against publicly available datasets and the performance has been compared to the current state of the art
Guest Editorial Introduction to the Special Issue on Best Biometrics Papers at ICPR 2020
No full textContext awareness in biometric systems and methods: State of the art and future scenarios
Full text linkIn the last decade, research in biometrics has been focused on augmenting the algorithmic performance to address a growing range of applications, not limited to person authentication/recognition. The concept of context awareness emerged as a possible key-factor for both performance optimization and operational adaptation of the capture, extraction, matching and decision stages. This may be particularly effective for multi-biometrics systems. The knowledge of the context in which a task is being performed, may provide useful information to the system in several manners. For example, it may allow to adapt to a specific environmental condition, such as shadow or light exposure. On the other hand, it may be possible to select the best available algorithm, among a given set to address the task at hand, which best performs within the given context. This paper aims to provide an overall vision of the main contributions available so far in the field of context-aware biometric systems and methods. The survey is not confined to a particular biometric modality or processing stage, but rather spans the state of the art of several biometric modalities and approaches. A taxonomy of context-aware biometric systems and methods is also proposed, along with a comparison of their features, aims and performances. The analysis will be complemented with a critical discussion about the state of the art also suggesting some future application scenarios. (C) 2018 Elsevier B.V. All rights reserved
Foveated Vision for Biologically Inspired Continuous Face Authentication
No full textIn everyday life whenever people observe, interact or speak to each other, visual attention is mostly directed toward the other person’s face, particularly to the eyes and the nearby periocular regions. This is naturally reflected when the user interacts with their mobile phones in several usual activities, such as web access, payments and video calls. For this reason, the functionality of mobile devices is strongly affected by the design of the user interface. In this chapter, we propose a biologically inspired approach for continuous user authentication based on the analysis of the ocular regions. The proposed system is based on a modified version of the HMAX visual processing module. HMAX is a hierarchical model which has been conceived to mimic the basic neural architecture of the ventral stream of the visual cortex. The original HMAX model consists of four layers: S1, C1, S2 and C2. S1 and C1 represent the responses to a bank of orientation-selective Gabor filters. S2 and C2 represent the responses of simple and complex cells to other textural features. The discrimination power of HMAX in recognizing classes of objects is invariant to rotation and scale. The C1 layer, which is mainly responsible for the scale and rotation invariance, is implemented using a max-pooling operation, which may lose some spatial information. To overcome this problem while preserving the maximal visual acuity and hence the localization accuracy, we propose to augment the model by applying a retinal log-polar mapping. The log-polar mapping is an approximation of the retino-cortical mapping that is performed by the early stages of the primate visual system. Due to the high density of the cones in the fovea, the log-polar approximation of the space-variant distribution model of the photoreceptors can only be applied outside the foveal region. Therefore, the log-polar mapping is added to the HMAX model as a complementary stage to process the peripheral region of the grabbed images. In order to demonstrate the feasibility of the proposed approach to mobile scenarios, experimental results obtained from publicly available databases and image streams grabbed from mobile devices will be presented
Foveated Vision for Deepface Recognition
No full textIn the last decade deep learning techniques have strongly influenced many aspects of computational vision. Many difficult vision tasks can now be performed by deploying a properly tailored and trained deep network. The enthusiasm for deep learning is unfortunately paired by the present lack of a clear understanding of how they work and why they provide such brilliant performance. The same applies to biometric systems. Deep learning has been successfully applied to several biometric recognition tasks, including face recognition. VGG-face is possibly the first deep convolutional network designed to perform face recognition, obtaining unsurpassed performance at the time it was firstly proposed. Over the last years, several and more complex deep convolutional networks, trained on very large, mainly private, datasets, have been proposed still elevating the performance bar also on quite challenging public databases, such as the Janus IJB-A and IJB-B. Despite of the progress in the development of such networks, and the advance in the learning algorithms, the insight on these networks is still very limited. For this reason, in this paper we analyse a biologically-inspired network based on the HMAX model, not with the aim of pushing the recognition performance further, but to better understand the representation space produced by including the retino-cortical mapping performed by the log-polar image resampling
An Investigation into Feature Level Fusion of Face and Fingerprint Biometrics
No full textntroduction The term “biometrics” defines the analysis of unique physiological or behavioral characteristics to verify the claimed identity of an individual. Biometric identification has eventually assumed a much broader relevance as a new technological solution toward more intuitive computer interfaces (Hong et al. 1999; Jain et al. 1999). Multibiometric systems Jain and Ross (2004) have been devised to overcome some of the limitations of unimodal biometric systems. In general terms, the combination of multiple biometric traits is operated by grouping multiple sources of information. These systems utilize more than one physiological or behavioral characteristic, or a combination of both, for enrollment and identification. For example, the problem of nonuniversality can be overcome, because multiple traits together always provide a sufficient population coverage. Multibiometrics also offers an efficient countermeasure to spoofing, because it would be difficult for an impostor to spoof multiple biometric traits of a genuine user simultaneously (Jain and Ross 2004). In some cases the sensor data can be corrupted or noisy, the use of multiple biometric traits always allow to reduce the effects of errors and noise in the data. Ross and Jain (2003) presented a wide overview of multimodal biometrics describing different possible levels of fusion, within several scenarios, modes of operation, integration strategies, and design issues
Alignment-Robust Cancelable Biometric Scheme for Iris Verification
No full textIn this paper, we propose a histogram of oriented gradient inspired cancelable biometrics-Random Augmented Histogram of Gradients (R HoG) for iris template protection. The proposed R HoG is built upon on two main components: 1) column vector random augmentation and 2) gradient orientation grouping mechanisms to transform the unaligned irisCode feature into the alignment-robust cancelable template. The alignment-robust property of the proposed R HoG enables the fast template comparison which is crucial for an efficient authentication process. Experiments were performed on CASIA-IrisV3-Internal and CASIA-IrisV4-Thousand datasets. The results demonstrate the proposed R HoG could achieve acceptable verification performance in both datasets. Other than that, the irreversibility and security properties are studied based on major security and privacy attacks in biometric system. Lastly, results from the benchmarking evaluation framework show the proposed method is satisfying the unlinkability property
Forensic Biometrics: Challenges, Innovation and Opportunities. In: Francese, S., S. P. King, R. (eds) Driving Forensic Innovation in the 21st Century. Springer, Cham.
No full textForensic science has always benefited from the adoption and exploitation of novel technologies to perform and analyze measurements at a crime scene and in the laboratory. Modern information technologies boosted many forensic procedures, such as accelerating and automating the comparison of fingerprints and fingermarks, and, recently, the analysis and comparison of images from human faces. Moreover, the recent advent of fast and performant Machine Learning (often dubbed AI) models, greatly improved the applicability of automatic face recognition to operational scenarios. However, even though technology has enabled the development of such systems, there are several hindering factors which must be taken into account. In this chapter the technological, legal and societal factors potentially enabling and fostering the development and application of automatic face recognition in forensic procedures are described and discussed. Also, the current issues and main concerns, restricting the mass-adoption of automatic face recognition technologies in forensic cases are presented. This chapter attempts not only to document both enablers and roadblockers of forensic face recognition, but also provides some promising research avenues and suggestions for a better application of these technologies in today’s society
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