322,888 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
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
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
Gender and ethnicity recognition based on visual attention-driven deep architectures
No full textMost of the time, when people observe, interact or speak to each other, they focus the attention on the ocular parts of the face. This daily life experience has a strong impact on the analysis of periocular facial regions. These facial regions may be exploited in order to identify individuals for several applications, including access control and services such as telebanking and electronic transactions. In this paper we suggest studying the efficiency of the periocular regions on gender and race prediction. Most researchers propose a local texture description based on LBP (Local Binary Pattern) and HoG (Histogram of Oriented Gradients) for the purpose of predicting gender. On the other hand, Deep learning techniques were proposed to predict the gender. However, this requires a huge labeled periocular data for gender which is not available. Also, the expressivity of gender and race can be decreased on the final representation of the Deep architectures comparing to the earlier stages. To overcome these points and for the aim of predicting gender and race, considering also the high impact of DCNNs (Deep Convolutional Neural Networks) techniques to solve several aspects in biometrics, we suggest a Deep architecture based on visual attention on the periocular part. The visual saliency extraction is based on primary layers’ activation by analyzing the feature-maps. We study how the visual attention-based features coupled to Deep Neural Networks can be used to discriminate between gender and race, hence extract a significant feature from periocular regions. Different pretrained architectures such as Alexnet and ResNet-50 were considered to extract visual saliency points or interest points. Several experiments were performed on periocular regions and a comparative study was conducted. The present results not only demonstrate the feasibility but also the robustness of the extracted interest points
An Hybrid Attention-Based System for the Prediction of Facial Attributes
No full textRecent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architecture, the HMAX. The architecture is tailored to predict attributes with different textural information and conveying different semantic meaning, such as attributes related and unrelated to the subject’s identity. Salient points on the face are extracted from the outputs of the S2 layer of the HMAX architecture and fed to a local texture characterization module based on LBP (Local Binary Pattern). The resulting feature vector is used to perform a binary classification on a set of pre-defined visual attributes. The devised system allows to distill a very informative, yet robust, representation of the imaged faces, allowing to obtain high performance but with a much simpler architecture as compared to a deep convolutional neural network. Several experiments performed on publicly available, challenging, large datasets demonstrate the validity of the proposed approach
On the correlation between human fixations, handcrafted and CNN features
No full textTraditional local image descriptors such as SIFT and SURF are based on processings similar to those that take place in the early visual cortex. Nowadays, convolutional neural networks still draw inspiration from the human vision system, integrating computational elements typical of higher visual cortical areas. Deep CNN's architectures are intrinsically hard to interpret, so much effort has been made to dissect them in order to understand which type of features they learn. However, considering the resemblance to the human vision system, no enough attention has been devoted to understand if the image features learned by deep CNNs and used for classification correlate with features that humans select when viewing images, the so-called human fixations, nor if they correlate with earlier developed handcrafted features such as SIFT and SURF. Exploring these correlations is highly meaningful since what we require from CNNs, and features in general, is to recognize and correctly classify objects or subjects relevant to humans. In this paper, we establish the correlation between three families of image interest points: human fixations, handcrafted and CNN features. We extract features from the feature maps of selected layers of several deep CNN's architectures, from the shallowest to the deepest. All features and fixations are then compared with two types of measures, global and local, which unveil the degree of similarity of the areas of interest of the three families. From the experiments carried out on ETD human fixations database, it turns out that human fixations are positively correlated with handcrafted features and even more with deep layers of CNNs and that handcrafted features highly correlate between themselves as some CNNs do
On a diophantine equation of Ayad and Kihel
No full textLet f(n) denote the number of relatively prime sets in {1; : : : ; n}. This is sequence A085945 in Sloane’s On-Line Encyclopedia of Integer Sequences. Motivated by a paper of Ayad and Kihel [1], we show that there are at most finitely many positive integers n such that f(n) is a perfect power of exponent > 1 of some other integer. We also show that the sequence {f(n)}n ≥ 1 is not holonomic; that is, it satisfies no recurrence relation of finite order with polynomial coefficients.Quaestiones Mathematicae 35(2012), 235–24
Diffusive author(s), cohesive author: Analysis of S/N (1994)
No full textThis study indicates the ways in which various aspects of the author(s) are brought forth in Dumb type’s performance art, the S/N production. Previous research has suggested a non-hierarchical organization of Dumb type and the absence of a “privileged author” in Dumb type’s collaborative work, S/N. However, the results that I have investigated from member’s interviews on the creative process of S/N along with my analysis of the recorded images of S/N, indicate a different aspect of the author(s). First, S/N was created through, so to speak, the collective ideas of the members of Dumb type. Further, S/N has at least nine quotations from previous performances, installations, and printed writings, besides the work-in-progress technique. Explicating one of the “author functions” as given by Michel Foucault, each text has plural subjects of the author. However, it has been revealed from members’ interviews that Teiji Furuhashi had a decision-making role in selecting the members’ ideas within the performance. Since then, S/N has had plural subjects of creation; however, Furuhashi is one of the subjects of creation along with the “privileged author.” S/N has plural authors (diffusive authors) yet at the same time, it has a “privileged author,” Teiji Furuhashi (cohesive author)
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
