Particle swarm optimization based fusion of near infrared and visible images for improved face verification

Raghavendra, R.; Dorizzi, Bernadette; Rao, Ashok; Kumar, G. Hemantha

doi:10.1016/j.patcog.2010.08.006

Cited by 85 publications

(31 citation statements)

References 11 publications

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“…The weights of individual classifier are calculated using the concept of PSO. Raghavendra et al [11] proposed a PSO based fusion of near infrared and visible image for improved face verification, in the first scheme PSO is used to calculate the optimum weight of a weighted linear combination of the coefficients and in the 2 nd scheme PSO is used to select the optimal fused feature of near infrared and visible image. L. Mezai and F.Hachouf [6] proposed a fusion of face and voice using PSO and belief function at score level fusion, in which the belief assignment is generated from the score of each modality using Denoeux and Appriou models.PSO is used to estimates the confidence factor, the fusion of weighted belief assignment is carried out using Dempster-Shafer(DS) and finally make decision making whether the claim user is genuine or not.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion

Modak¹,

Jha²

2017

IJCA

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Unimodal biometric systems have several inherent problems such as intra-class variation, noisy-sensor data, spoofing attacks and non-universality. To overcome this limitation multibiometric is a good option where we can use two or more individual modalities. In this paper we propose a multibiometric system to enhance the performance and minimize the error rate using Ant colony optimization (ACO) based on score level fusion. This work extracts the feature from two different modalities namely face and iris (left/right). In this work we use ACO as an optimization technique to select the fusion parameter like weights for the different biometric matcher and fusion rule which is used further for score level fusion. The experimental results show that the multibiometric system using ACO based on sum rule is outperform than the other fusion rule like product, tanh and exponential sum.

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Section: Related Workmentioning

confidence: 99%

Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion

Modak¹,

Jha²

2017

IJCA

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“…The successful application of image fusion will lead to improved performance of military surveillance [2], remote sensing, medical imaging [14], face recognition [3,5,7,10,11], situational awareness, agriculture, concealed weapon detection [20] and satellite imaging.…”

Section: Applicationsmentioning

confidence: 99%

“…Visual band imaging is highly matured and successful due to the advancement of sensor technology, availability of computing power, and high digital storage capacity [4,5,6]. However, the image processing task and applications are challenging if the visual images obtained under non-ideal environments [7,8,9]. The end result depends on the extent of external light source, which sometimes might be absent in environments when there are heavy clouds, fog, rain, snow, smoke, darkness, shadows and at night-time.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence based Optimization in Thermal Image Fusion using Dual Tree Discrete Wavelet Transform

Madheswari¹,

Venkateswaran²

2015

Proceedings of the 2015 Asia International Conference on Quantitative InfraRed Thermography

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In this paper, we propose a Particle Swarm Optimized image fusion framework in Discrete Wavelet Transform domain that combines the thermal image with the visual image to obtain a single informative fused image. Dual tree Discrete Wavelet Transform (DT-DWT) is applied for feature selection and particle swarm optimization (PSO) technique is used to obtain the optimized image. In the fusion process, an optimized weighting factor has been used to form a new composite image with maximized Entropy and minimized Root Mean Square Error (RMSE). Simulation results indicate that the proposed fusion framework results in better quality of the fused image.

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“…It computes vertical (C ) and horizontal (R) gradients information and can be expressed as [33] (13) and the total activity level of the image is computed as SF = C 2 + R 2 (14) 5 Experimental results The image pairs must be registered before the fusion processes. Therefore the experiments were conducted on the images that were registered beforehand.…”

Section: Spatial Frequency (Sf)mentioning

confidence: 99%

New optimised region‐based multi‐scale image fusion method for thermal and visible images

Aslantaş

Bendeş

Kurban

et al. 2014

IET Image Processing

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On constructing a fused image by employing only individual pixels or a set of pixels within a small neighbourhood of the images (SIs) acquired from the same scene, pixel-based fusion techniques suffer from some drawbacks, such as blurring effects, high sensitivity to noise and misregistration. To overcome these drawbacks, this study proposes a new region-based image fusion method for thermal and visible images. Since different regions with certain properties need to be emphasised differently in the fused image, the corresponding regions of the SIs are optimally merged to obtain the fused image by employing multiple weighting factors (WFs). To improve the quality of the fused images, WFs were optimised by employing the differential evolution algorithm. Furthermore, a new quality metric was also developed to measure the quality of the fused images during the optimisation process. Experimental results show the feasibility of the proposed method.

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Particle swarm optimization based fusion of near infrared and visible images for improved face verification

Cited by 85 publications

References 11 publications

Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion

Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion

Swarm Intelligence based Optimization in Thermal Image Fusion using Dual Tree Discrete Wavelet Transform

New optimised region‐based multi‐scale image fusion method for thermal and visible images

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