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Since the seminal paper of Ratha et al. in 2001 that introduced cancelable biometrics, inner permutation of biometric templates has been widely suggested as one of the basic components to protect biometric data against compromised or cross-checking between two databases. In this paper, we study the case of iris biometrics where an inner permutation corresponds to shuffling the bits of a template in...
Availability of a single training sample (STS) or degraded set (DS) of training and testing samples restricts the success of face recognition in real-world applications. We propose a unified framework for handling both these challenges simultaneously by using a data dictionary, which is a combination of training dictionary and intra-class variation dictionary. The training dictionary is assembled...
Ear classification refers to the process by which an input ear image is assigned to one of several pre-defined classes based on a set of features extracted from the image. In the context of large-scale ear identification, where the input probe image has to be compared against a large set of gallery images in order to locate a matching identity, classification can be used to restrict the matching process...
The well-known Smith-Waterman (SW) algorithm is a high-sensitivity method for local alignments. However, SW is expensive in terms of both execution time and memory usage, which makes it impractical in many applications. Some heuristics are possible but at the expense of losing sensitivity. Fortunately, previous research have shown that new computing platforms such as GPUs and FPGAs are able to accelerate...
In this paper we propose the use of formal methods to model malicious code behavior. The paradigm shift in malware detection from conventional, signature-based static methods to evaluating dynamic system behavior is motivated by the rising number and ever-increasing sophistication of malware currently in the wild. Because of advanced polymorphic and metamorphic techniques, a purely signature-based...
Core region detection of handwritten cursive words is an important step towards their automatic recognition. Several preprocessing operations such as height normalization, slant estimation etc. Are often based on this core region. This is particularly useful for word recognition of major Indian scripts, which have large character sets. The main parts of majority of these characters belong to the core...
This paper proposes a system for text-independent writer identification based on Arabic handwriting using only 21 features. Gaussian Mixture Models (GMMs) are used as the core of the system. GMMs provide a powerful representation of the distribution of features extracted using a fixed-length sliding window from the text lines and words of a writer. For each writer a GMM is built and trained using...
Recently the theories of sparse representation (SR) and dictionary learning (DL) have brought much attention and become powerful tools for pattern recognition and computer vision. Due to the fact that images can be represented in a sparse and compressible way with respect to some dictionaries, these theories have shown successful applications in many different areas including face recognition, image...
Speaker identification attempts to determine the best possible match from a group of certain speakers, for any given input speech signal. The text-independent speaker identification system does the task to identify the person who speaks regardless of what is said. The first step in speaker identification is the extraction of features. In this proposed method, the Bessel features are used as an alternative...
This study investigates the degree that the performance of a likelihood ratio (LR)-based forensic text comparison (FTC) system improves by using logistic-regression fusion on LRs that were separately estimated by three different procedures, involving lexical features, word-based N-grams and character-based N-grams. This study uses predatory chatlog messages. The number of words used for modelling...
Vision-based hand gesture recognition relies on the extraction of features describing the hand, and the appropriate set of features is usually selected in an empirical manner. We propose in this article a systematic selection of the best features to be considered. An iterative sequential forward feature selection (SFS) approach is proposed to combine the features with the highest recognition rate...
Facial makeup is one of the problems that are caused decrease in the performance of face recognition algorithms. In this paper, a novel method is proposed to handle facial makeup in face recognition. The combining of Local Binary Pattern (LBP) and Dual-Tree Complex Wavelet Transform (DT-CWT) is applied to face image for feature extraction. Then, by combining entire of LBP and DT-CWT feature vectors,...
The efficient search in unsorted database is extremely important for our everydays' applications. This type of search is useful in communication networks as well, since routing can be considered as a search in the database of the routing table. From the viewpoint of quantum computing, the not quantum-based solutions are named as classical. The search could be very time-consuming and computationally...
The soft biometric traits such as distinctive skin markings, tattoos based on its visual and demographic of tattoo are mostly used to identify a suspect or a victim in forensic sciences. Nevertheless, in many scenarios, police investigators type in the description of a suspect's tattoo based on text search, therefore these classifications make it difficult to recognize associated tattoo, for example,...
Symmetry Assessment by Finite Expansion (SAFE) is a novel description of image information by means of Generalized Structure Tensor. It represents orientation data in neighbourhood of key points projected onto the space of harmonic functions creating a geometrically interpretable feature of low dimension. The proposed feature has built in quality metrics reflecting accuracy of the extracted feature...
This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients from a single-channel intra-cranial electroencephaolograph (iEEG) recording. Instead of extracting features from the EEG signal, first the EEG signal is filtered by a prediction error filter (PEF) to compute a prediction error signal. A two-level wavelet decomposition of the prediction error signal...
In this study we propose a novel atrial activity-based method for atrial fibrillation (AF) identification that detects the absence of normal sinus rhythm (SR) P-waves from the surface ECG. The proposed algorithm extracts nine features from P-waves during SR and develops a statistical model to describe the distribution of the features. The Expectation-Maximization algorithm is applied to a training...
Intrapartum fetal surveillance for early detection of fetal acidosis in clinical practice focuses on reducing neonatal morbidity via early detection. It is the subject of on going research studies attempting notably to improve detection performance by reducing false positive rate. In that context, the present contribution tailors to fetal heart rate variability analysis a graph-based dimensionality...
This paper proposes a key motion spotting method which is designed to locate the motion of interest (or key motion) from a database of motion sequences. As a key motion could be just a subsequence of the stored motion sequence, the proposed method differs much from the general methods of content-based retrieval of motion sequences, which searches from the database the individual sequences similar...
Registration of aerial images is a necessary step as valuable information for studying; monitoring, forecasting and managing natural resources can be obtained from it. This paper presents an algorithm for registration of aerial images using orthogonal moment invariants. Initially reference aerial image and test aerial image are resized to same size and converted to gray scale and contrast enhancement...
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