Showing papers in "Signal, Image and Video Processing in 2011"

A survey on super-resolution imaging

Abstract: The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘low-resolution’ images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review of SR image and video reconstruction methods developed in the literature and highlight the future research challenges. The SR image approaches reconstruct a single higher-resolution image from a set of given lower-resolution images, and the SR video approaches reconstruct an image sequence with a higher-resolution from a group of adjacent lower-resolution image frames. Furthermore, several SR applications are discussed to contribute some insightful comments on future SR research directions. Specifically, the SR computations for multi-view images and the SR video computation in the temporal domain are discussed.

A comprehensive assessment of the structural similarity index

Abstract: In recent years the structural similarity index has become an accepted standard among image quality metrics. Made up of three components, this technique assesses the visual impact of changes in image luminance, contrast, and structure. Applications of the index include image enhancement, video quality monitoring, and image encoding. As its status continues to rise, however, so do questions about its performance. In this paper, it is shown, both empirically and analytically, that the index is directly related to the conventional, and often unreliable, mean squared error. In the first evaluation, the two metrics are statistically compared with one another. Then, in the second, a pair of functions that algebraically connects the two is derived. These results suggest a much closer relationship between the structural similarity index and mean squared error.

A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram

Abstract: Ventricular fibrillation (VF) is the most serious variety of arrhythmia which requires quick and accurate detection to save lives. In this paper, we propose a new time domain algorithm, called threshold crossing sample count (TCSC), which is an improved version of the threshold crossing interval (TCI) algorithm for VF detection. The algorithm is based on an important feature of the VF signal which relies on the random behavior of the electrical heart vector. By two simple operations: comparison and count, the technique calculates an effective measure which is used to separate life-threatening VF from other heart rhythms. For assessment of the performance of the algorithm, the method is applied on the complete MIT-BIH arrhythmia and CU databases, and a promising good performance is observed. Seven other classical and new VF detection algorithms, including TCI, have been simulated and comparative performance results in terms of different quality parameters are presented. The TCSC algorithm yields the highest value of the area under the receiver operating characteristic curve (AUC). The new algorithm shows strong potential to be applied in clinical applications for faster and accurate detection of VF.

Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density

Abstract: In a less constrained capture of iris images to build a high-speed iris recognition system, the design of a robust and fast iris segmentation method is important. In this paper, a new iris segmentation technique based on the Fourier spectral density is proposed for noisy frontal view eye images captured with minimum cooperation from the subjects. The proposed segmentation method is not an iterative technique and it runs in deterministic time. The computational complexity of the proposed method is found to be significantly lower than the existing approaches based on integro-differential operator, Hough transform and active contour. The basic idea underlying the proposed method is to localize the limbic and pupil boundaries using the Fourier spectral density. The performance studies on a recently created iris database, called UBIRIS (Proenca and Alexandre in Lect Notes Comput Sci 3617:970–977, 2005) containing defocused, reflection-contained and eyelid-occluded iris images in visible spectral range, show that the proposed method is much faster than the existing methods and simultaneously achieves good segmentation accuracy.

Appearance-based periocular features in the context of face and non-ideal iris recognition

Abstract: Developing newer approaches to deal with non-ideal scenarios in face and iris biometrics has been a key focus of research in recent years. The same reason motivates the study of the periocular biometrics as its use has a potential of significantly impacting the iris- and face-based recognition. In this paper, we explore the utility of the various appearance features extracted from the periocular region from different perspectives: (i) as an independent biometric modality for human identification, (ii) as a tool that can aid iris recognition in non-ideal situations in the near infra-red (NIR) spectrum, and (iii) as a possible partial face recognition technique in the visible spectrum. We employ a local appearance-based feature representation, where the periocular image is divided into spatially salient patches, appearance features are computed for each patch locally, and the local features are combined to describe the entire image. The images are matched by computing the distance between the corresponding feature representations using various distance metrics. The evaluation of the periocular region-based recognition and comparison to face recognition is performed in the visible spectrum using the FRGC face dataset. For fusion of the periocular and iris modality, we use the MBGC NIR face videos. We demonstrate that in certain non-ideal conditions encountered in our experiments, the periocular biometrics is superior to iris in the NIR spectrum. Furthermore, we also demonstrate that recognition performance of the periocular region images is comparable to that of face in the visible spectrum.

Image quality assessment based on S-CIELAB model

Abstract: This paper proposes a new image quality assessment framework which is based on color perceptual model. By analyzing the shortages of the existing image quality assessment methods and combining the color perceptual model, the general framework of color image quality assessment based on the S-CIELAB color space is presented. The S-CIELAB color model, a spatial extension of CIELAB, has an excellent performance for mimicking the perceptual processing of human color vision. This paper incorporates excellent color perceptual characteristics model with the geometrical distortion measurement to assess the image quality. First, the reference and distorted images are transformed into S-CIELAB color perceptual space, and the transformed images are evaluated by existing metric in three color perceptual channels. The fidelity factors of three channels are weighted to obtain the image quality. Experimental results achieved on LIVE database II shows that the proposed methods are in good consistency with human subjective assessment results.

A theoretical discussion on the foundation of Stone’s blind source separation

Abstract: This paper discusses the theoretical foundation of Stone’s BSS (Stone in Neural Comput 13:1559–1574, 2001; Stone in Independent Component Analysis: A Tutorial Introduction, A Bradford Book, London, 2004), and it proposes a novel BSS approach based on second-order statistics of the responses of two different linear filters to source signals. The proposed approach which includes Stone’s BSS as a special case helps us to understand how generalized eigenvalue decomposition (GEVD) concludes separating vectors in Stone’s BSS. It obtains the separating vectors by simultaneous diagonalization of covariance matrices of two different linear filters responses to the mixtures. The two employed linear filters are selected dependent on source signals structures under the assumption that they have different responses to source signals. Here, two FIR filters with coefficients selected in an opposite probabilistic way have been suggested for the proposed BSS. The proposed BSS method has been compared with Stone’s BSS, SOBI and AMUSE over speech and image mixtures in different noise levels.

Occlusion detection and gait silhouette reconstruction from degraded scenes

Abstract: Gait, which is defined as the style of walking of a person, has been recognized as a potential biometric feature for identifying human beings. The fundamental nature of gait biometric of being unconstrained and captured often without a subject’s knowledge or co-operation has motivated many researchers over the last one decade. However, all of the approaches found in the literature assume that there is little or no occlusion present at the time of capturing gait images, both during training and during testing and deployment. We look into this challenging problem of gait recognition in the presence of occlusion. A novel approach is proposed, which first detects the presence of occlusion and accordingly extracts clean and unclean gait cycles from the whole input sequence. In the second step, occluded silhouette frames are reconstructed using Balanced Gaussian Process Dynamical Model (BGPDM). We evaluated our approach on a new data set TUM-IITKGP featuring inter-object occlusion. Algorithms have also been tested on CMU’s Mobo data set by introducing synthetic occlusion of different degrees. The proposed approach shows promising result on both the data sets.

Fuzzy logic-based automatic contrast enhancement of satellite images of ocean

Abstract: In this paper, we evaluate the conventional contrast enhancement techniques [histogram equalization (HE), adaptive HE] and the recent gray-level grouping method and the fuzzy logic method in order to find out which of these is well suited for automatic contrast enhancement for satellite images of the ocean, obtained from a variety of sensors. All the techniques evaluated were based on the principle of transforming the skewed histogram of the original image into a uniform histogram. The performance of the different contrast enhancement algorithms are evaluated based on the visual quality and the Tenengrad criterion. The inter comparison of different techniques was carried out on a standard low-contrast image and also three different satellite images with different characteristics. Based on our study, we advocate that a modified fuzzy logic method elucidated in this paper is well suited for contrast enhancement of low-contrast satellite images of the ocean.

Wavelet shrinkage: unification of basic thresholding functions and thresholds

Abstract: This work addresses the unification of some basic functions and thresholds used in non-parametric estimation of signals by shrinkage in the wavelet domain. The soft and hard thresholding functions are presented as degenerate smooth sigmoid-based shrinkage functions. The shrinkage achieved by this new family of sigmoid-based functions is then shown to be equivalent to a regularization of wavelet coefficients associated with a class of penalty functions. Some sigmoid-based penalty functions are calculated, and their properties are discussed. The unification also concerns the universal and the minimax thresholds used to calibrate standard soft and hard thresholding functions: these thresholds pertain to a wide class of thresholds, called the detection thresholds. These thresholds depend on two parameters describing the sparsity degree for the wavelet representation of a signal. It is also shown that the non-degenerate sigmoid shrinkage adjusted with the new detection thresholds is as performant as the best up-to-date parametric and computationally expensive method. This justifies the relevance of sigmoid shrinkage for noise reduction in large databases or large size images.

User authentication based on foot motion

Abstract: In nearly all current systems, user authentication mechanism is one time and static. Although such type of user authentication is sufficient for many types of applications, in some scenarios, continuous or periodic re-verification of the identity is desirable, especially in high-security application. In this paper, we study user authentication based on 3D foot motion, which can be suitable for periodic identity re-verification purposes. Three-directional (3D) motion of the foot (in terms of acceleration signals) is collected using a wearable accelerometer sensor attached to the ankle of the person. Ankle accelerations from three directions (up-down, forward-backward and sideways) are analyzed for person authentication. Applied recognition method is based on detecting individual cycles in the signal and then finding best matching cycle pair between two acceleration signals. Using experimental data from 30 subjects, obtained EERs (Equal Error Rates) were in the range of 1.6–23.7% depending on motion directions and shoe types. Furthermore, by combining acceleration signals from 2D and 3D and then applying fusing techniques, recognition accuracies could be improved even further. The achieved performance improvements (in terms of EER) were up to 68.8%.

Single image super-resolution using self-examples and texture synthesis

Abstract: An algorithm for single image super-resolution based on example-based super-resolution and example-based texture synthesis is proposed. While many other techniques for single image super-resolution are mainly effective on edges, the proposed algorithm enhances both edges and texture detail. The algorithm does not use an additional example database as it uses self-examples to synthesize new detail and texture, assuming that images contain a sufficient amount of self-similarity. The texture synthesis component of the algorithm enables the re-synthesis of texture at the output resolution to achieve super-resolution. The algorithm aims to create plausible, visually pleasing detail rather than reconstructing the true high-resolution image. Experimental results for natural images confirm the algorithms ability to create visually pleasing results, but also indicate that its performance is highly content dependent. Future efforts will be aimed at improving the robustness of the method.

Heartbeat biometrics for human authentication

Abstract: Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like the electrocardiogram (ECG) of a person is unique and secure. In this paper, we propose an authentication technique based on Radon transform. Here, ECG wave is considered as an image and Radon transform is applied on this image. Standardized Euclidean distance is applied on the Radon image to get a feature vector. Correlation coefficient between such two feature vectors is computed to authenticate a person. False Acceptance Ratio of the proposed system is found to be 2.19% and False Rejection Ratio is 0.128%. We have developed two more approaches based on statistical features of an ECG wave as our ground work. The result of proposed technique is compared with these two approaches and also with other state-of-the-art alternatives.

B-spline wavelets for signal denoising and image compression

Abstract: In this paper we propose to develop novel techniques for signal/image decomposition, and reconstruction based on the B-spline mathematical functions. Our proposed B-spline based multiscale/resolution representation is based upon a perfect reconstruction analysis/synthesis point of view. Our proposed B-spline analysis can be utilized for different signal/imaging applications such as compression, prediction, and denoising. We also present a straightforward computationally efficient approach for B-spline basis calculations that is based upon matrix multiplication and avoids any extra generated basis. Then we propose a novel technique for enhanced B-spline based compression for different image coders by preprocessing the image prior to the decomposition stage in any image coder. This would reduce the amount of data correlation and would allow for more compression, as will be shown with our correlation metric. Extensive simulations that have been carried on the well-known SPIHT image coder with and without the proposed correlation removal methodology are presented. Finally, we utilized our proposed B-spline basis for denoising and estimation applications. Illustrative results that demonstrate the efficiency of the proposed approaches are presented.

A closed form design method for the two-channel quadrature mirror filter banks

Abstract: This paper presents a simple and efficient closed form method for designing two-channel linear phase quadrature mirror filter (QMF) banks with prescribed stopband attenuation and channel overlap. The proposed method is based on optimum passband edge frequency, which is calculated using empirical formulas instead of using optimization algorithm. Different window functions are used to design the prototype filter for QMF banks. When compared to other existing methods, the proposed method reduces computation time (CPU time) and amplitude distortion (e am ), which results in a simpler and efficient design procedure for the applications where the design must be carried out in real or quasi-real-time. Several design examples are included to illustrate the proposed method and its improved performances over other exiting methods. An application of the proposed method is considered in the area of subband coding of ultrasound image.

On the use of dynamic features in face biometrics: recent advances and challenges

Abstract: The way a person is moving his/her head and facial parts (such as the movements of the mouth when a person is talking) defines so called facial dynamics and characterizes personal behaviors. An emerging direction in automatic face analysis consists of also using such dynamic cues, in addition to facial structure, in order to enhance the performance of static image-based methods. This is inspired by psychophysical and neural studies indicating that behavioral characteristics do also provide valuable information to face analysis in the human visual system. This survey article presents the motivations, reviews the recent developments and discusses several other important issues related to the use of facial dynamics in computer vision. As a case study of using facial dynamics, two LBP-based baseline methods are considered and experimental results in different face-related problems, including face recognition, gender recognition, age estimation and ethnicity classification are reported and discussed. Furthermore, remaining challenges are highlighted and some promising directions are pointed out.

Personal identification using feature and score level fusion of palm- and fingerprints

Abstract: The ever increasing demand of security has resulted in wide use of Biometric systems. Despite overcoming the traditional verification problems, the unimodal systems suffer from various challenges like intra class variation, noise in the sensor data etc, affecting the system performance. These problems are effectively handled by multimodal systems. In this paper, we present multimodal approach for palm- and fingerprints by feature level and score level fusions (sum and product rules). The proposed multi-modal systems are tested on a developed database consisting of 440 palm- and fingerprints each of 55 individuals. In feature level fusion, directional energy-based feature vectors of palm- and fingerprint identifiers are combined to form joint feature vector that is subsequently used to identify the individual using a distance classifier. In score level fusion, the matching scores of individual classifiers are fused by sum and product rules. Receiver operating characteristics curves are formed for unimodal and multimodal systems. Equal Error Rate (EER) of 0.538% for feature level fusion shows best performance compared to score level fusion of 0.6141 and 0.5482% of sum and product rules, respectively. Multimodal systems, however, significantly outperform unimodal palm- and fingerprints identifiers with EER of 2.822 and 2.553%, respectively.

A joint 2D AM–FM estimation based on higher order Teager–Kaiser energy operators

Abstract: In this work, an image demodulation algorithm based on two-dimensional higher order Teager–Kaiser (TK) operators is presented. We show quantitatively and qualitatively that the introduction of higher orders in TK operator improves amplitude modulation (AM) and frequency modulation (FM) estimation results, compared to classical approaches such as the Discrete Energy Separation Algorithm (DESA) or the Analytic Signal (AS) method. Indeed, for a wide class of images, obtained demodulation errors for both the amplitude and frequency are numerically lower than the obtained ones with the DESA and AS method. The proposed method is illustrated on both synthetic and real images. Moreover, it turns out for some real images that the algorithm is very efficient in the sense that it tracks the most significant part in images and segments regions of interests, particularly, the AM counterpart. Finally, an application of our approach to the segmentation of mines’ shadows in Sonar images is presented. This is very important for both civil and military applications.

Test generation algorithm for analog systems based on support vector machine

Abstract: In some methods for test generation, an analog device under test (DUT) is treated as a discrete-time digital system by placing it between a digital-to-analog converter and an analog-to-digital converter. Then the test patterns and responses can be performed and analyzed in the digital domain. We propose a novel test generation algorithm based on a support vector machine (SVM). This method uses test patterns derived from the test generation algorithm as input stimuli, and sampled output responses of the analog DUT for classification and fault detection. The SVM is used for classification of the response space. When the responses of normal circuits are similar to those of faulty circuits (i.e., the latter have only small parametric faults), the response space is mixed and traditional algorithms have difficulty in distinguishing the two groups. However, the SVM provides an effective result. This paper also proposes an algorithm to calculate the test sequence for input stimuli using the SVM results. Numerical experiments prove that this algorithm can enhance the precision of test generation.

A MSE optimized polynomial equalizer for 16QAM and 64QAM constellation

Abstract: Recently, a Maximum Entropy (MaxEnt) algorithm and its derivation called WNEW algorithm were presented by the same author. It was shown that the MaxEnt and WNEW algorithm have improved equalization performance compared with Godard’s, reduced constellation algorithm and the sign reduced constellation algorithm. In this paper, a new equalization method is proposed for the 16QAM and 64QAM input constellation based on the WNEW algorithm which is extended with some polynomials of the equalized output and optimized with the mean square error criteria. According to simulation results, the new equalization method leads to over 15 dB advantage in the residual Intersymbol Interference compared to the results presented by Godard, 10 dB advantage compared with the WNEW algorithm and 5 dB advantage compared with the MaxEnt algorithm.

A matching criterion for motion compensation in the temporal coding of video signal

Abstract: Video data have spatial as well as temporal redundancy and motion estimation plays a vital role in the removal of temporal redundancy of video data. Block matching techniques are mostly used and generally the matching criterion in these block matching techniques is the mean absolute error (MAE). Though MAE-based approach is simple and less complex, it does not give better prediction specially for large motion video inputs with contrast variation. In this manuscript, a new block matching criterion has been suggested and experimentally compared with three existing methods in terms of four parameters—average MAE/pixel, average search points/block, average peak signal to noise ratio (PSNR) and average number of bits/pixel value. Proposed criterion gives nearly 75% less average error than conventional MAE. An increase of nearly 16% in average PSNR value and 37% reduction in average bits/pixel value in comparison to MAE has been observed for the proposed criterion. Further, these criterions have also been evaluated for quality/compression ratio which is nearly 80% more for the proposed criterion than corresponding MAE metric.

A criterion for measuring the separability of clusters and its applications to principal component analysis

Abstract: Reducing the dimensionality of the data as a pre-processing step of a pattern recognition application is very important. While applying the well-known Principal Component Analysis to a large data set, it is not always clear how to choose the dimension of the reduced feature space so that it would reflect appropriately the inherent dimensionality of the original feature space. We propose a simple criterion to select the reduced dimension for the sake of separability or classifiability. Essentially, the proposed criterion should be applicable to many types of data. Extensive implementation has been done to test the validity and efficiency of the proposed method.

Clinical decision support system for early prediction of Down syndrome fetus using sonogram images

Abstract: In this paper, the segmentation and extraction of features from ultrasound second trimester fetal images have been presented for early detection of Down syndrome. The region of interest and the edges of the segmented region have been obtained using mean shift analysis and Canny operator, respectively. The prime features such as the nasal bone, the palate and the frontal bone have been segmented for estimating the nasal bone length and frontomaxillary facial angle (FMF). It is observed from the results that the rate of growth of nasal bone length is poor and the FMF angle has been found to increase above 85° for fetus with trisomy 21. This analysis may help the physician for better clinical diagnosis.

Statistical modeling for the detection, localization and extraction of text from heterogeneous textual images using combined feature scheme

Abstract: Discriminating between the text and non text regions of an image is a complex and challenging task. In contrast to Caption text, Scene text can have any orientation and may be distorted by the perspective projection. Moreover, it is often affected by variations in scene and camera parameters such as illumination, focus, etc. These variations make the design of unified text extraction from various kinds of images extremely difficult. This paper proposes a statistical unified approach for the extraction of text from hybrid textual images (both Scene text and Caption text in an image) and Document images with variations in text by using carefully selected features with the help of multi level feature priority (MLFP) algorithm. The selected features are combinedly found to be the good choice of feature vectors and have the efficacy to discriminate between text and non text regions for Scene text, Caption text and Document images and the proposed system is robust to illumination, transformation/perspective projection, font size and radially changing/angular text. MLFP feature selection algorithm is evaluated with three common ML algorithms: a decision tree inducer (C4.5), a naive Bayes classifier, and an instance based K-nearest neighbour learner and effectiveness of MLFP is shown by comparing with three feature selection methods with benchmark dataset. The proposed text extraction system is compared with the Edge based method, Connected component method and Texture based method and shown encouraging result and finds its major application in preprocessing for optical character recognition technique and multimedia processing, mobile robot navigation, vehicle license detection and recognition, page segmentation and text-based image indexing, etc.

Noise reduction in high dynamic range images

Abstract: Multiple images with different exposures are used to produce a high dynamic range (HDR) image. Sometimes high-sensitivity setting is needed for capturing images in low light condition as in an indoor room. However, current digital cameras do not produce a high-quality HDR image when noise occurs in low light condition or high-sensitivity setting. In this paper, we propose a noise reduction method in generating HDR images using a set of low dynamic range (LDR) images with different exposures, where ghost artifacts are effectively removed by image registration and local motion information. In high-sensitivity setting, motion information is used in generating a HDR image. We analyze the characteristics of the proposed method and compare the performance of the proposed and existing HDR image generation methods, in which Reinhard et al.’s global tone mapping method is used for displaying the final HDR images. Experiments with several sets of test LDR images with different exposures show that the proposed method gives better performance than existing methods in terms of visual quality and computation time.

Shot boundary detection in the presence of fire flicker and explosion using stationary wavelet transform

Abstract: Detection of fire in video for fire alarm systems has been studied by many researchers, but detection of shot boundaries under fire, flicker and explosion (FFE) is one of the under-studied areas. In thriller movies, FFE occur more often than other special effects and lead to false detection of shot boundary. We tested major metrics used for detection of shot boundaries under FFE for various movies. It is observed that for almost all metrics, precision is low due to false positives caused by FFE. We propose an algorithm based on cross-correlation coefficient, stationary wavelet transform and combination of local and adaptive thresholds for detection of shot boundaries under FFE. The proposed algorithm is tested on three movies, and experimental results validate the effectiveness of our proposed method in terms of better recall and precision.

An optimized transmultiplexer using combinational window functions

Abstract: This paper proposes an efficient approach for the design of M-channel maximally decimated near-perfect reconstruction (NPR) type transmultiplexer. Cosine modulation is used to design the synthesis and analysis sections of the transmultiplexer. The prototype filter is designed by using high sidelobe falloff rate (SLFOR) combinational window functions. A bisection-type optimization algorithm has been applied to minimize the interference parameters like inter-channel interference (ICI) and inter-symbol interference (ISI). The proposed algorithm has certain advantages than earlier reported work. The algorithm is of generalized nature, independent of the window function used in the design of the prototype filter. Second, it is fast and computationally efficient as only a single parameter is used as a variable which provides almost uniform interference level in all subchannels. Design examples are included for comparison with earlier reported work. Very small values of ICI and ISI have been obtained by using variable and combinational window functions.

Multivariate kernel diffusion for surface denoising

Abstract: In this paper, we introduce a 3D mesh denoising method based on kernel density estimation. The proposed approach is able to reduce the over-smoothing effect and effectively remove undesirable noise while preserving prominent geometric features of a 3D mesh such as curved surface regions, sharp edges, and fine details. The experimental results demonstrate the effectiveness of the proposed approach in comparison to existing mesh denoising techniques.

Detection of quadratic phase coupling from human EEG signals using higher order statistics and spectra

Abstract: Interactions among neural signals in different frequency components have become a focus of strong interest in biomedical signal processing The bispectrum is a method to detect the presence of quadratic phase coupling (QPC) between different frequency bands in a signal The traditional way to quantify phase coupling is by means of the bicoherence index (BCI), which is essentially a normalized bispectrum The main disadvantage of the BCI is that the determination of significant QPC becomes compromised with noise To mitigate this problem, a statistical approach that combines the bispectrum with an improved surrogate data method to determine the statistical significance of the phase coupling is introduced The method was first tested on two simulation examples It was then applied to the human EEG signal that has been recorded from the scalp using international 10–20 electrodes system The frequency domain method, based on normalized spectrum and bispectrum, describes frequency interactions associated with nonlinearities occurring in the observed EEG

An approach to hand biometrics in mobile devices

Abstract: This paper focuses on hand biometrics applied to images acquired from a mobile device. The system offers the possibility of identifying individuals based on features extracted from hand pictures obtained with a low-quality camera embedded on a mobile device. Furthermore, the acquisitions have been carried out regardless illumination control, orientation, distance to camera, and similar aspects. In addition, the whole system has been tested with an owned database. Finally, the results obtained (6.0% ± 0.2) and the algorithm structure are both promising in relation to a posterior mobile implementation.