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Convolutional neural networks have significantly boosted the performance of face recognition in recent years due to its high capacity in learning discriminative features. In order to enhance the discriminative power of the deeply learned features, we propose a new supervision signal named marginal loss for deep face recognition. Specifically, the marginal loss simultaneously minimises the intra-class...
The sparse representation based classification (SRC) performs not very well for small sample data. A discriminative common vector dictionary based SRC is introduced in this paper to address this issue. The contribution of this paper is that the dictionary of the proposed method is constructed by the discriminative common vector per class. The common vector represents the invariant property of each...
Steganography is the art of covered writing or hidden writing. The main concern of steganography (image hiding) methods is to embed a secret image into a cover image in such a way that the cover should remain as similar as possible to its original version. In addition the cover image should remain robust with respect to usual attacks. In this research, we present a method that tries to cover all above...
In this paper, we analyze the problems produced by temporal variations of infrared face images when used in face recognition systems. The temporal variations present in thermal face images are mainly due to different environmental conditions, physiological changes of the subjects, and differences of the infrared detectors’ responsivity at the time of the capture, which affect the performance of infrared...
Long intergenic non-coding RNAs (lincRNAs) are associated with a wide variety of human diseases. Piles of data about the lincRNAs are becoming available, thanks to the High Throughput Sequencing (HTS) platforms, which open opportunity for cutting-edge machine learning and data mining approaches to analyze the disease association better. However, there are only a few in silico association inference...
In this paper, we investigate the effect of transfer of emotion-rich features between source and target networks on classification accuracy and training time in a multimodal setting for vision based emotion recognition. First, we propose emosource-a 6-layer Deep Belief Network (DBN), trained on popular emotion corpora for emotion classification. Second, we propose two 6-layer DBNs — emotarget and...
Dissimilarities in equal error rates (EERs) of multiple matchers heavily influence the performance of multi-biometric systems. A normalization technique aims at improving the recognition rate of such a system. In view of this, in this paper, an anchored normalization technique, referred to as improved anchored min-max (IAMM) technique for a multimodal biometric system, is developed. In the proposed...
We propose a novel, efficient way to identify things to which it is difficult to attach identifiers such as markers, barcodes and RFID tags. With our proposed method, a dot of metallic or glitter ink is attached onto an object and is identified as unique by matching a microscopic image of the dot with a database. The ink is an inexpensive consumer product. It is a mixture of a basic ink with micro...
Non-rigid point set registration is a fundamental problem for many computer vision technologies. In this paper, we proposed a new non-rigid point set registration method based on coherent spatial mapping (CSM) and local geometrical constraint. Our central idea is to express each point as a weighted sum of several nearest neighbors and the same relation holds after the transformation. The registration...
In the field of automatic face recognition, transformations of facial features due to aging cause a problem. Due to small amounts of extracted features, the identity verification can be difficult. The feature-based methods that are present in the literature are still being developed, with unsatisfactory results caused by high rates of false matching. In this paper we present a new method of matching...
Shape outliers can seriously affect the statistical analysis of the shape variations usually performed by the Principal Component Analysis PCA. This paper presents an algorithm for outliers detection and shape restoration as a new strategy for robust statistical shape analysis. The proposed framework is founded on an elastic metric in the shape space to cope with the nonlinear shape variability. The...
Technological processes in the raw material industry often operate continuously and requirements for high robustness and fail safe operation of the information and control systems are defined for them to fall into the fault-tolerant category. A critical element of fault-tolerant solutions ensures the continuity of data in databases. Data on the course of technological processes assume the nature he...
This paper presents a person identification technique that uses information from person's shadow, and is robust to appearance changes caused by variations of clothes and carried objects. The technique uses invisible lights and resulting shadows and has advantages from undetected sensing. The shadows on the ground obtained through illumination by multiple lights can be considered as silhouettes captured...
Methods based on Local Binary Patterns have been used successfully in a wide range of texture classification tasks. A restriction shared by all methods based on Local Binary Patterns is the high sensitivity to signal scale. In recent work we presented a general framework for scale-adaptive computation of Local Binary Patterns, improving the accuracy in texture classification scenarios involving varying...
In view of rapidly growing digital music collections and ubiquitous music consumption, the development of technologies for identifying, browsing, and managing audio content has become a major strand of research. In this context, audio identification (ID) systems for identifying audio recordings by means of short query audio clips have become of commercial relevance. In this paper, we take a closer...
In this paper, we propose a novel robust and efficient minutia-based fingerprint matching algorithm. There are two key contributions. First, we apply a set of global level minutia dependent features, i.e., the qualities that measure the reliabilities of the extracted minutiae and the area of overlapping regions between the query and template images of fingerprints. The implementation of these easy-to-get...
The large amount of research on multimodal systems raises an important question: can we extract additional information from unimodal systems? In this paper, we propose a rank-based score normalization framework that addresses this problem when multi-sample galleries are avail-able. The main idea is to partition the matching scores into subsets and normalize each subset independently. In addition,...
This paper points out the fact that object recognition methods are usually too complex for everyday life scenes. A robot helping humans in daily activities will need to recognize hundreds of different objects. In order to filter out unlikely models during recognition we propose the use of a cascade of simple visual descriptors. Our experiments use two global descriptors : spatial and color minimum...
Building information modelling (BIM) is only a tool in the procurement of a building, but it is changing the way the whole process is undertaken. Having a single model which is used by all disciplines requires a different management structure and by consequence a restructuring of the resources required to complete a project.These changes are encompassed in Integrated Practice and more recently Integrated...
The development of an open architecture for multimedia data integration and visualization in template based distributed applications is a challenge for the scientific researches. Because of characteristics of multimedia data, their management and querying techniques are unlike than those of traditional data. We can increase the potential use of multimedia data across various applications by storing...
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