90 research outputs found
A novel multispectral and 2.5D/3D image fusion camera system for enhanced face recognition
The fusion of images from the visible and long-wave infrared (thermal) portions of the spectrum
produces images that have improved face recognition performance under varying lighting conditions.
This is because long-wave infrared images are the result of emitted, rather than reflected,
light and are therefore less sensitive to changes in ambient light. Similarly, 3D and 2.5D images
have also improved face recognition under varying pose and lighting. The opacity of glass to
long-wave infrared light, however, means that the presence of eyeglasses in a face image reduces
the recognition performance.
This thesis presents the design and performance evaluation of a novel camera system which is
capable of capturing spatially registered visible, near-infrared, long-wave infrared and 2.5D depth
video images via a common optical path requiring no spatial registration between sensors beyond
scaling for differences in sensor sizes. Experiments using a range of established face recognition
methods and multi-class SVM classifiers show that the fused output from our camera system not
only outperforms the single modality images for face recognition, but that the adaptive fusion
methods used produce consistent increases in recognition accuracy under varying pose, lighting
and with the presence of eyeglasses
On Using High-Definition Body Worn Cameras for Face Recognition from a Distance
Recognition of human faces from a distance is highly desirable for law-enforcement. This paper evaluates the use of low-cost, high-definition (HD) body worn video cameras for face recognition from a distance. A comparison of HD vs. Standard-definition (SD) video for face recognition from a distance is presented. HD and SD videos of 20 subjects were acquired in different conditions and at varying distances. The evaluation uses three benchmark algorithms: Eigenfaces, Fisherfaces and Wavelet Transforms. The study indicates when gallery and probe images consist of faces captured from a distance, HD video result in better recognition accuracy, compared to SD video. This scenario resembles real-life conditions of video surveillance and law-enforcement activities. However, at a close range, face data obtained from SD video result in similar, if not better recognition accuracy than using HD face data of the same range
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Diagnosis of liver disease by computer- assisted imaging techniques: A literature review
Copyright © 2022 The authors. Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician's or doctor's experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abnormalities. Machine learning and deep learning algorithms and models play also a big role in this area. In this article, we tried to review the techniques which are utilized in different stages of CAD systems and pursue the methods used in preprocessing, extracting, and selecting features and classification. Also, different techniques are used to segment and analyze the liver ultrasound medical images, which is still a challenging approach to how to use these techniques and their technical and clinical effectiveness as a global approach
A Novel Approach For Face Recognition Using Fusion Of Local Gabor Patterns
For face recognition, Gabor features are effectively used. But, only a few approaches used Gabor phase features and they are performing worse than the Gabor magnitude features. To determine the potential of Gabor phase and its fusion with magnitude for face recognition, in this paper, we have proposed local Gabor XOR pattern (LGXP) operator, which encode Gabor phase. Then we introduce block-based Fisher’s linear discriminant (BFLD) for reduce dimensionality of proposed operator and at same time discriminative power also get enhanced. At last, by using BFLD we fuse Gabor phase and Gabor magnitude for face recognition. We evaluate our method for FERET database. Also, we perform comparative experimental studies of different local patterns.DOI:http://dx.doi.org/10.11591/ijece.v2i3.279
Human face recognition under degraded conditions
Comparative studies on the state of the art feature extraction and classification techniques for human face recognition under low resolution problem, are proposed in this work. Also, the effect of applying resolution enhancement, using interpolation techniques, is evaluated. A gradient-based illumination insensitive preprocessing technique is proposed using the ratio between the gradient magnitude and the current intensity level of image which is insensitive against severe level of lighting effect. Also, a combination of multi-scale Weber analysis and enhanced DD-DT-CWT is demonstrated to have a noticeable stability versus illumination variation. Moreover, utilization of the illumination insensitive image descriptors on the preprocessed image leads to further robustness against lighting effect. The proposed block-based face analysis decreases the effect of occlusion by devoting different weights to the image subblocks, according to their discrimination power, in the score or decision level fusion. In addition, a hierarchical structure of global and block-based techniques is proposed to improve the recognition accuracy when different image degraded conditions occur. Complementary performance of global and local techniques leads to considerable improvement in the face recognition accuracy. Effectiveness of the proposed algorithms are evaluated on Extended Yale B, AR, CMU Multi-PIE, LFW, FERET and FRGC databases with large number of images under different degradation conditions. The experimental results show an improved performance under poor illumination, facial expression and, occluded images
A Survey of Iris Recognition System
The uniqueness of iris texture makes it one of the reliable physiological biometric traits compare to the other biometric traits. In this paper, we investigate a different level of fusion approach in iris image. Although, a number of iris recognition methods has been proposed in recent years, however most of them focus on the feature extraction and classification method. Less number of method focuses on the information fusion of iris images. Fusion is believed to produce a better discrimination power in the feature space, thus we conduct an analysis to investigate which fusion level is able to produce the best result for iris recognition system. Experimental analysis using CASIA dataset shows feature level fusion produce 99% recognition accuracy. The verification analysis shows the best result is GAR = 95% at the FRR = 0.1
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