Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach...Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.展开更多
Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods ...Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information extraction.Existing defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera configuration.Hence,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned limitations.This paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur regions.We argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred regions.The fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the image.Additionally,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy images.Experimental results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur detection.Evaluation and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.展开更多
提出一种基于差值局部方向模式的人脸特征表示方法(difference local directional pattern,简称DLDP):首先,通过Kirsch掩模卷积运算,为每个像素计算8个方向的边缘响应值;然后,计算8个相邻边缘响应值的强度差,前k个最突出的强度差对应的...提出一种基于差值局部方向模式的人脸特征表示方法(difference local directional pattern,简称DLDP):首先,通过Kirsch掩模卷积运算,为每个像素计算8个方向的边缘响应值;然后,计算8个相邻边缘响应值的强度差,前k个最突出的强度差对应的方向编码为1,其他方向编码为0,形成一个8位二进制数表示对应的DLDP模式;此外,针对高分辨率的Kirsch掩模单纯考虑方向性而没有考虑像素位置权重的问题,提出相应的掩模权值设计方法;最后,把每幅图像划分成多个不重叠的局部图像块,通过统计图像块上不同DLDP模式个数生成相应的子直方图,所有子直方图被串联起来表示一幅人脸图像.实验结果表明,该方法在光照、表情、姿态和遮挡方面获得了较好的结果,尤其针对遮挡情况,表现更为突出.展开更多
文摘Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.
基金This work was supported and funded by the Directorate ASR&TD of UET-Taxila.
文摘Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information extraction.Existing defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera configuration.Hence,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned limitations.This paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur regions.We argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred regions.The fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the image.Additionally,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy images.Experimental results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur detection.Evaluation and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.
文摘提出一种基于差值局部方向模式的人脸特征表示方法(difference local directional pattern,简称DLDP):首先,通过Kirsch掩模卷积运算,为每个像素计算8个方向的边缘响应值;然后,计算8个相邻边缘响应值的强度差,前k个最突出的强度差对应的方向编码为1,其他方向编码为0,形成一个8位二进制数表示对应的DLDP模式;此外,针对高分辨率的Kirsch掩模单纯考虑方向性而没有考虑像素位置权重的问题,提出相应的掩模权值设计方法;最后,把每幅图像划分成多个不重叠的局部图像块,通过统计图像块上不同DLDP模式个数生成相应的子直方图,所有子直方图被串联起来表示一幅人脸图像.实验结果表明,该方法在光照、表情、姿态和遮挡方面获得了较好的结果,尤其针对遮挡情况,表现更为突出.
