Face detection of ubiquitous surveillance images for biometric security from an image enhancement perspective

Iqbal, Kashif; Odetayo, Michael O.; James, Anne

doi:10.1007/s12652-012-0134-y

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…Works have been done to resolve the above factors by normalizing color, transformation of color spaces, multiple color channel ratios and eliminating luminance components. In addition to the above challenging factors, cameras may also result in poor visibility of images that makes the recognition of objects either difficult or impossible [3]. Since, the quality of images is very much affected by movement of objects, low resolution cameras, lighting condition environments etc.…”

Section: Related Workmentioning

confidence: 99%

“…It is a linear scaling function that is applied to the image pixel values. The pixels values of an enhanced image of the corresponding original image can be obtained by using following basic equation [3]:…”

Section: ) Pre-processing Stepmentioning

confidence: 99%

“…The robustness and the reliability of a face recognition system are reliant to a great extent upon the ability of the face detection systems accuracy [2] -one of the biometric methods that are used in security systems [3]. It is the preliminary step of face recognition process.…”

Section: Introductionmentioning

confidence: 99%

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Face detection for video summary using enhancement-based fusion strategy under varying illumination conditions

Mishra¹,

Subban²

2014

2014 International Conference on Science Engineering and Management Research (ICSEMR)

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A biometric-based techniques emerge as the promising approach for most of the real-time applications including security systems, video surveillances, human-computer interaction and many more. Among all biometric methods, face recognition offers more benefits as compared to others. Diagnosing human faces and localizing them in images or videos is the priori step of tracking and recognizing. But the performance of face detection is limited by certain factors namely lighting conditions, pose variation, occlusions, low resolution images and complex background. To overcome the problems, this paper examines a fusion strategy in the enhancement-based skincolor segmentation approach that can improve the performance of face detection algorithm. The method is robust against complex background, ethnicity and lighting variations. The method consists of three steps. The first step receives spatial transform techniques in parallel to enhance the contrast of the image, change the color space of the enhanced images to YCbCr, apply skin segmentation technique and yield the binary segmented images. The second step ascertains the weight of accuracy (WoA) of each of the segmented image and fed it into the fusion strategy to get the final skin detected region. Finally, the last step localizes the human face. The methodology is not constrained to just frontal face identification. However it is invariant with the diverse head postures, enlightment condition, and size of faces. The experimental result demonstrates the improvement in the accuracy and precision along with the reduction in FPR as compared to other enhancement classifiers.

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Section: Related Workmentioning

confidence: 99%

Section: ) Pre-processing Stepmentioning

confidence: 99%

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Face detection for video summary using enhancement-based fusion strategy under varying illumination conditions

Mishra¹,

Subban²

2014

2014 International Conference on Science Engineering and Management Research (ICSEMR)

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“…In such case, it is difficult to segment or detect the boundaries of the abnormal structures/lesions or blood vessels presented in these poor images. Low resolution could be improved by some super-resolution technologies Wei et al 2017), and yet enhancing low contrast should be performed by image enhancement methods (Iqbal et al 2014;Tao et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare

Nie

Cao

et al. 2018

J Ambient Intell Human Comput

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With the rapid growth of medical big data, medical signal processing measurement techniques are facing severe challenges. Enormous medical images are constantly generated by various health monitoring and sensing devices, such as ultrasound, MRI machines. Hence, based on pulse coupled neural network (PCNN) and the classical visual receptive field (CVRF) with the difference of two Gaussians (DOG), a contrast enhancement of MRI image is suggested to improve the accuracy of clinical diagnosis for smarter mobile healthcare. As one premise, the parameters of DOG are estimated from the fundamentals of CVRF; then the PCNN parameters in image enhancement are estimated eventually with the help of DOG. As a result, the MRI images can be enhanced adaptively. Due to the exponential decay of the dynamic threshold and the pulses coupling among neurons, PCNN effectively enhances the contrast of low grey levels in MRI image. Moreover, because of the inhibitory effects from inhibitory region in CVRF, PCNN also effectively preserves the structures such as edges for enhanced results. Experiments on several MRI images show that the proposed method performs better than other methods by improving contrast and preserving structures well.

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“…Nowadays, the development of information security is burgeoning (Li and Kim 2010;Li et al 2014;Castiglione et al 2015;Esposito et al 2013), and face recognition technology (FRT) has a variety of potential applications in information security (Iqbal et al 2014). During the past decades, principal component analysis (PCA) and linear discriminant analysis (LDA) have been two most popular subspace techniques used in the appearance-based approaches for face recognition, such as Eigenfaces (Turk and Pentland 1991) based upon PCA, Fisherfaces (Belhumeur et al 1997) and LDA-based methods have given promising results, they are linear projection methods, whereas face recognition is a highly nonlinear problem owing to image variations such as pose, illumination, and facial expression.…”

Section: Introductionmentioning

confidence: 99%

Fisher discriminant analysis based on kernel cuboid for face recognition

Liu

Zhang

2015

Soft Comput

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This paper builds the concept of kernel cuboid, and proposes a new kernel-based image feature extraction method for face recognition. The proposed method deals with a face image in a block-wise manner, and independently performs kernel discriminant analysis in every block set, using kernel cuboid instead of kernel matrix. Experimental results on the ORL and UMIST face databases show the effectiveness and scalability of the proposed method.

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Face detection of ubiquitous surveillance images for biometric security from an image enhancement perspective

Cited by 9 publications

References 21 publications

Face detection for video summary using enhancement-based fusion strategy under varying illumination conditions

Face detection for video summary using enhancement-based fusion strategy under varying illumination conditions

Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare

Fisher discriminant analysis based on kernel cuboid for face recognition

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