Optimization of a Biometric System Based on Acoustic Images

Fuente, Alberto Izquierdo; Val, Lara del; Calvo, Juan José; Mateos, Mariano Raboso

doi:10.1155/2014/780835

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…In order to have a population sample as general as possible, the subjects whose acoustic images were used have different morphological characteristics, as shown in Table 6 . In this case, unlike previous tests [ 13 , 18 ], acoustic images of each subject were obtained on different days and with them wearing different clothes. In this way, the system will be able to classify the subjects according to who they actually are, without clothes being a distinctive factor.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

“…Based on the conclusions of previous works [ 18 ], a new system that employs P = 3 spatial positions and F = 9 frequencies is defined. This system generates P i acoustic profiles, associated to subject i and formed by P·F = 27 images.…”

Section: System Descriptionmentioning

confidence: 99%

“…In a later work [ 18 ], the authors analyzed the contribution of each acoustic image—associated with a frequency and position—to the performance of the biometric system, finding that each image provides different degrees of information. Two main conclusions were obtained: Each set of images associated to certain frequency provides different information, improving the system performance, thus, the number of frequencies used should be increased.…”

Section: Introductionmentioning

confidence: 99%

“…Since high dimensionality of acoustic profiles exponentially increases the computational burden of SVM classifiers, preprocessing and feature extraction techniques have been designed and implemented to improve the classifier performance. This new system is based on the results obtained in previous studies [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

Val

Fuente

Villacorta

et al. 2015

Sensors

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Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

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Section: Analysis Of Resultsmentioning

confidence: 99%

Section: System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

Val

Fuente

Villacorta

et al. 2015

Sensors

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“…The authors of this paper have experience in the design [6] and development of acoustic imaging systems, based in acoustic arrays, used in many different fields, such as detection and tracking systems [7,8], Ambient Assisted Living [9], or biometric identification systems [10,11,12]. The arrays used were ULA (Uniform Linear Array), formed by acoustic sensors distributed uniformly along a line.…”

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of a Scalable and Reconfigurable Multi-Platform System for Acoustic Imaging

Izquierdo

Villacorta

Val

et al. 2016

Sensors

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This paper proposes a scalable and multi-platform framework for signal acquisition and processing, which allows for the generation of acoustic images using planar arrays of MEMS (Micro-Electro-Mechanical Systems) microphones with low development and deployment costs. Acoustic characterization of MEMS sensors was performed, and the beam pattern of a module, based on an 8 × 8 planar array and of several clusters of modules, was obtained. A flexible framework, formed by an FPGA, an embedded processor, a computer desktop, and a graphic processing unit, was defined. The processing times of the algorithms used to obtain the acoustic images, including signal processing and wideband beamforming via FFT, were evaluated in each subsystem of the framework. Based on this analysis, three frameworks are proposed, defined by the specific subsystems used and the algorithms shared. Finally, a set of acoustic images obtained from sound reflected from a person are presented as a case study in the field of biometric identification. These results reveal the feasibility of the proposed system.

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A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

Abdulameer

Abdullah

Othman

2014

The Scientific World Journal

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Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

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Optimization of a Biometric System Based on Acoustic Images

Cited by 5 publications

References 17 publications

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

Design and Evaluation of a Scalable and Reconfigurable Multi-Platform System for Acoustic Imaging

A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

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