FARO: FAce Recognition against Occlusions and Expression Variations

FARO: FAce Recognition Against Occlusions and Expression Variations Maria De Marsico, Member, IEEE, Michele Nappi, and Daniel Riccio Abstract—Face recognition is widely considered as one of the most promising biometric techniques, allowing high recognition rates without being too intrusive. Many approaches have been presented to solve this special pattern recognition problem, also addressing the challenging cases of face changes, mainly occurring in expression, illumination, or pose. On the other hand, less work can be found in literature that deals with partial occlusions (i.e., sunglasses and scarves). This paper presents FAce Recognition against Occlusions and Expression Variations (FARO) as a new method based on partitioned iterated function systems (PIFSs), which is quite robust with respect to expression changes and partial occlusions. In general, algorithms based on PIFSs compute a map of self-similarities inside the whole input image, searching for correspondences among small square regions. However, traditional algorithms of this kind suffer from local distortions such as occlusions. To overcome such limitation, information extracted by PIFS is made local by working independently on each face component (eyes, nose, and mouth). Distortions introduced by likely occlusions or expression changes are further reduced by means of an ad hoc distance measure. In order to experimentally confirm the robustness of the proposed method to both lighting and expression variations, as well as to occlusions, FARO has been tested using AR-Faces database, one of the main benchmarks for the scientific community in this context. A further validation of FARO performances is provided by the experimental results produced on Face Recognition Grand Challenge database

CABALA: Collaborative Architectures based on Biometric Adaptable Layers and Activities

The lack of communication and of dynamic adaptation to working settings often hinder stable performances of subsystems of present multibiometric architectures. The calibration phase often uses a specific training set, so that (sub)systems are tuned with respect to well determined conditions. In this work we investigate the modular construction of systems according to CABALA (Collaborative Architectures based on Biometric Adaptable Layers and Activities) approach. Different levels of flexibility and collaboration are supported. The computation of system reliability (SRR), for each single response of each single subsystem, allows to address temporary decrease of accuracy due to adverse conditions (light, dirty sensors, etc.), by possibly refusing a poorly reliable response or by asking for a new recognition operation. Subsystems can collaborate at a twofold level, both in returning a jointly determined answer, and in co-evolving to tune to changing conditions. At the first level, single-biometric subsystems implement the N-Cross Testing Protocol: they work in parallel, but exchange information to reach the final response. At an higher level of interdependency, parameters of each subsystem can be dynamically optimized according to the behavior of their companions. To this aim, an additional Supervisor Module analyzes the single results and, in our present implementation, modifies the degree of reliability required from each subsystem to accept its future responses. The paper explores different combinations of these novel strategies. We demonstrate that as component collaboration increases, the same happens to both the overall system accuracy and to the ability to identify unstable subsystems. (C) 2011 Elsevier Ltd. All rights reserved

S-nitrosothiol-derived nitric oxide delivery vehicles: synthesis and detection

The bioactivity of nitric oxide (NO) is endogenously transduced by S-nitrosothiols (RSNOs), a class of NO donor that may decompose via thermal, photolytic, or reductive pathways. Recent research has focused on employing RSNOs as NO delivery agents for biomedical applications. As part of my doctoral work, I have designed NO-releasing sol-gel-derived materials using RSNOs. Thiol functionalities are readily incorporated throughout silica scaffolds via the hydrolysis and co-condensation of mercaptopropyltrimethoxysilane and alkoxysilanes. After nitrosation, NO storage levels up to 4.40 mu mol mg-1 may be achieved. As anticipated, the NO release is dependent on heat, light, and/or copper concentration. For particle synthesis, a high degree of control over monodispersity and size may be obtained using controlled silane addition rates. Additionally, greater water concentrations during synthesis decrease particle size without altering NO storage. To prolong NO release, tertiary RSNO functionalities were incorporated within xerogel films by hydrolysis and co-condensation of a novel tertiary thiol-bearing silane with alkoxy- and alkylalkoxysilanes. Nitrosation resulted in NO storage up to 1.78 mu mol cm-2 dependent on the concentration of silane precursors and coating thicknesses. These materials exhibited enhanced stability due to steric hindrance surrounding the nitroso group, as evidenced by release of only ~11% of the stored NO after 24 h at 37 degC. Photolysis may be used to trigger NO release from the films at physiological temperature irrespective of soak time. Indeed, NO fluxes were greater under irradiation than in the dark (e.g., ~23 vs. 3 pmol cm-2 s-1, respectively). A benefit of such NO release was demonstrated whereby ~90% less bacteria adhered to RSNO-modified xerogels when irradiated. In the last phase of my dissertation research, RSNO decomposition to NO by visible photolysis was coupled with a NO-permselective electrode to develop a RSNO electrochemical sensor. Increasing the irradiation time enhanced sensitivity up to 1.56 nA mu M-1 and lowered the theoretical detection limit to 30 nM for low molecular weight RSNOs. Detection of a nitrosated protein was also possible, but at decreased sensitivity (0.11 nA mu M-1). This methodology was demonstrated by measuring RSNOs in plasma, illustrating the potential to elucidate the basal levels of RSNOs in circulation

Gigapixel Histopathological Image Analysis using Attention-based Neural Networks

Although CNNs are widely considered as the state-of-the-art models in various applications of image analysis, one of the main challenges still open is the training of a CNN on high resolution images. Different strategies have been proposed involving either a rescaling of the image or an individual processing of parts of the image. Such strategies cannot be applied to images, such as gigapixel histopathological images, for which a high reduction in resolution inherently effects a loss of discriminative information, and in respect of which the analysis of single parts of the image suffers from a lack of global information or implies a high workload in terms of annotating the training images in such a way as to select significant parts. We propose a method for the analysis of gigapixel histopathological images solely by using weak image-level labels. In particular, two analysis tasks are taken into account: a binary classification and a prediction of the tumor proliferation score. Our method is based on a CNN structure consisting of a compressing path and a learning path. In the compressing path, the gigapixel image is packed into a grid-based feature map by using a residual network devoted to the feature extraction of each patch into which the image has been divided. In the learning path, attention modules are applied to the grid-based feature map, taking into account spatial correlations of neighboring patch features to find regions of interest, which are then used for the final whole slide analysis. Our method integrates both global and local information, is flexible with regard to the size of the input images and only requires weak image-level labels. Comparisons with different methods of the state-of-the-art on two well known datasets, Camelyon16 and TUPAC16, have been made to confirm the validity of the proposed model.Comment: The manuscript was submitted to a peer-review journal on January 27t

Entropy Based Template Analysis in Face Biometric Identification Systems

The accuracy of a biometric matching algorithm relies on its ability to better separate score distributions for genuine and impostor subjects. However, capture conditions (e.g. illumination or acquisition devices) as well as factors related to the subject at hand (e.g. pose or occlusions) may even take a generally accurate algorithm to provide incorrect answers. Techniques for face classification are still too sensitive to image distortion, and this limit hinders their use in large-scale commercial applications, which are typically run in uncontrolled settings. This paper will join the notion of quality with the further interesting concept of representativeness of a biometric sample, taking into account the case of more samples per subject. Though being of excellent quality, the gallery samples belonging to a certain subject might be very (too much) similar among them, so that even a moderately different sample of the same subject in input will cause an error. This seems to indicate that quality measures alone are not able to guarantee good performances. In practice, a subject gallery should include a sufficient amount of possible variations, in order to allow correct recognition in different situations. We call this gallery feature representativeness. A significant feature to consider together with quality is the sufficient representativeness of (each) subject’s gallery. A strategy to address this problem is to investigate the role of the entropy, which is computed over a set of samples of a same subject. The paper will present a number of applications of such a measure in handling the galleries of the different users who are registered in a system. The resulting criteria might also guide template updating, to assure gallery representativeness over time

Face Authentication using Speed Fractal Technique

In this paper, a new fractal based recognition method, Face Authentication using Speed Fractal Technique (FAST), is presented. The main contribution is the good compromise between memory requirements, execution time and recognition ratio. FAST is based on Iterated Function Systems (IFS) theory, largely studied in still image compression and indexing, but not yet widely used for face recognition. Indeed, Fractals are well known to be invariant to a large set of global transformations. FAST is robust with respect to meaningful variations in facial expression and to the small changes of illumination and pose. Another advantage of the FAST strategy consists in the speed up that it introduces. The typical slowness of fractal image compression is avoided by exploiting only the indexing phase, which requires time O(D log (D)), where D is the size of the domain pool. Lastly, the FAST algorithm compares well to a large set of other recognition methods, as underlined in the experimental results

BIRD: Watershed Based IRis Detection for mobile devices

Communications with a central iris database system using common wireless technologies, such as tablets and smartphones, and iris acquisition out of the field are important functionalities and capabilities of a mobile iris identification device. However, when images are acquired by means of mobile devices under uncontrolled acquisition conditions, noisy images are produced and the effectiveness of the iris recognition system is significantly conditioned. This paper proposes a technique based on watershed transform for iris detection in noisy images captured by mobile devices. The method exploits the information related to limbus to segment the periocular region and merges its score with the iris' one to achieve greater accuracy in the recognition phase

Normal Maps vs. Visible Images: Comparing Classifiers and Combining Modalities

This work investigates face recognition based on normal maps, and the performance improvement that can be obtained when exploiting it within a multimodal system, where a further independent module processes visible images. We first propose a technique to align two 3D models of a face by means of normal maps, which is very fast while providing an accuracy comparable to well-known and more general techniques such as Iterative Closest Point (ICP). Moreover, we propose a matching criterion based on a technique which exploits difference maps. It does not reduce the dimension of the feature space, but performs a weighted matching between two normal maps. In the second place, we explore the range of performance soffered by different linear and non linear classifiers, when applied to the normal maps generated from the above aligned models. Such experiments highlight the added value of chromatic information contained in normal maps. We analyse a solid list of classifiers which we reselected due to their historical reference value (e.g. Principal Component Analysis) or to their good performances in the bidimensional setting (Linear Discriminant Analysis, Partitioned Iterated Function Systems). Last but not least, we perform experiments to measure how different ways of combining normal maps and visible images can enhance the results obtained by the single recognition systems, given that specific characteristics of the images are taken into account. For these last experiments we only consider the classifier giving the best average results in the preceding ones, namely the PIFS-based one

Nitric oxide release: Part I. Macromolecular scaffolds

The roles of nitric oxide (NO) in physiology and pathophysiology merit the use of NO as a therapeutic for certain biomedical applications. Unfortunately, limited NO payloads, too rapid NO release, and the lack of targeted NO delivery have hindered the clinical utility of NO gas and low molecular weight NO donor compounds. A wide-variety of NO-releasing macromolecular scaffolds has thus been developed to improve NO’s pharmacological potential. In this tutorial review, we provide an overview of the most promising NO release scaffolds including protein, organic, inorganic, and hybrid organic-inorganic systems. The NO release vehicles selected for discussion were chosen based on their enhanced NO storage, tunable NO release characteristics, and potential as therapeutics