The Experts below are selected from a list of 6837 Experts worldwide ranked by ideXlab platform
Xiaojun Qi - One of the best experts on this subject based on the ideXlab platform.
-
face recognition under varying Illuminations using logarithmic fractal dimension based complete eight local directional patterns
Neurocomputing, 2016Co-Authors: Mohammad Reza Faraji, Xiaojun QiAbstract:Face recognition under Illumination is really challenging. This paper proposes an effective method to produce Illumination-Invariant features for images with various levels of Illumination. The proposed method seamlessly combines adaptive homomorphic filtering, simplified logarithmic fractal dimension, and complete eight local directional patterns to produce Illumination-Invariant representations. Our extensive experiments show that the proposed method outperforms two of its variant methods and nine state-of-the-art methods, and achieves the overall face recognition accuracy of 99.47%, 94.55%, 99.53%, and 86.63% on Yale B, extended Yale B, CMU-PIE, and AR face databases, respectively, when using one image per subject for training. It also outperforms the compared methods on the Honda UCSD video database using five images per subject for training and considering all necessary steps including face detection, landmark localization, face normalization, and face matching to recognize faces. Our evaluations using receiver operating characteristic (ROC) curves also verify the proposed method has the best verification and discrimination ability compared with other peer methods.
Farajimohammad Reza - One of the best experts on this subject based on the ideXlab platform.
-
face recognition under varying Illuminations using logarithmic fractal dimension based complete eight local directional patterns
Neurocomputing, 2016Co-Authors: Farajimohammad RezaAbstract:Face recognition under Illumination is really challenging. This paper proposes an effective method to produce Illumination-Invariant features for images with various levels of Illumination. The pro...
Mohammad Reza Faraji - One of the best experts on this subject based on the ideXlab platform.
-
face recognition under varying Illuminations using logarithmic fractal dimension based complete eight local directional patterns
Neurocomputing, 2016Co-Authors: Mohammad Reza Faraji, Xiaojun QiAbstract:Face recognition under Illumination is really challenging. This paper proposes an effective method to produce Illumination-Invariant features for images with various levels of Illumination. The proposed method seamlessly combines adaptive homomorphic filtering, simplified logarithmic fractal dimension, and complete eight local directional patterns to produce Illumination-Invariant representations. Our extensive experiments show that the proposed method outperforms two of its variant methods and nine state-of-the-art methods, and achieves the overall face recognition accuracy of 99.47%, 94.55%, 99.53%, and 86.63% on Yale B, extended Yale B, CMU-PIE, and AR face databases, respectively, when using one image per subject for training. It also outperforms the compared methods on the Honda UCSD video database using five images per subject for training and considering all necessary steps including face detection, landmark localization, face normalization, and face matching to recognize faces. Our evaluations using receiver operating characteristic (ROC) curves also verify the proposed method has the best verification and discrimination ability compared with other peer methods.
Euisik Yoon - One of the best experts on this subject based on the ideXlab platform.
-
a5 1ms low latency face detection imager with in memory charge domain computing of machine learning classifiers
Symposium on VLSI Technology, 2021Co-Authors: Hyunsoo Song, Juan Salinas, Sungyun Park, Euisik YoonAbstract:We present a CMOS imager for low-latency face detection empowered by parallel imaging and computing of machine-learning (ML) classifiers. The energy-efficient parallel operation and multi-scale detection eliminate image capture delay and significantly alleviate backend computational loads. The proposed pixel architecture, composed of dynamic samplers in a global shutter (GS) pixel array, allows for energy-efficient in-memory charge-domain computing of feature extraction and classification. The Illumination-Invariant detection was realized by using log-Haar features. A prototype 240×240 imager achieved an on-chip face detection latency of 5.1ms with a 97.9% true positive rate and 2% false positive rate at 120fps. Moreover, a dynamic nature of in-memory computing allows an energy efficiency of 419pJ/pixel for feature extraction and classification, leading to the smallest latency-energy product of 3.66ms∙nJ/pixel with digital backend processing.
-
a 5 1ms low latency face detection imager with in memory charge domain computing of machine learning classifiers
Symposium on VLSI Circuits, 2021Co-Authors: Hyunsoo Song, Juan Salinas, Sungyun Park, Euisik YoonAbstract:We present a CMOS imager for low-latency face detection empowered by parallel imaging and computing of machine-learning (ML) classifiers. The energy-efficient parallel operation and multi-scale detection eliminate image capture delay and significantly alleviate backend computational loads. The proposed pixel architecture, composed of dynamic samplers in a global shutter (GS) pixel array, allows for energy-efficient in-memory charge-domain computing of feature extraction and classification. The Illumination-Invariant detection was realized by using log-Haar features. A prototype 240×240 imager achieved an on-chip face detection latency of 5.1ms with a 97.9% true positive rate and 2% false positive rate at 120fps. Moreover, a dynamic nature of in-memory computing allows an energy efficiency of 419pJ/pixel for feature extraction and classification, leading to the smallest latency-energy product of 3.66ms∙nJ/pixel with digital backend processing.
Kwanghoon Sohn - One of the best experts on this subject based on the ideXlab platform.
-
real time Illumination Invariant lane detection for lane departure warning system
Expert Systems With Applications, 2015Co-Authors: Jongin Son, Hunjae Yoo, Sanghoon Kim, Kwanghoon SohnAbstract:Invariant property of lane color under various Illuminations is utilized for lane detection.Computational complexity is reduced using vanishing point detection and adaptive ROI.Datasets for evaluation include various environments from several devices.Simulation demo demonstrate fast and powerful performance for real-time applications. Lane detection is an important element in improving driving safety. In this paper, we propose a real-time and Illumination Invariant lane detection method for lane departure warning system. The proposed method works well in various Illumination conditions such as in bad weather conditions and at night time. It includes three major components: First, we detect a vanishing point based on a voting map and define an adaptive region of interest (ROI) to reduce computational complexity. Second, we utilize the distinct property of lane colors to achieve Illumination Invariant lane marker candidate detection. Finally, we find the main lane using a clustering method from the lane marker candidates. In case of lane departure situation, our system sends driver alarm signal. Experimental results show satisfactory performance with an average detection rate of 93% under various Illumination conditions. Moreover, the overall process takes only 33ms per frame.
-
mahalanobis distance cross correlation for Illumination Invariant stereo matching
IEEE Transactions on Circuits and Systems for Video Technology, 2014Co-Authors: Seungryong Kim, Bumsub Ham, Bongjoe Kim, Kwanghoon SohnAbstract:A robust similarity measure called the Mahalanobis distance cross-correlation (MDCC) is proposed for Illumination-Invariant stereo matching, which uses a local color distribution within support windows. It is shown that the Mahalanobis distance between the color itself and the average color is preserved under affine transformation. The MDCC converts pixels within each support window into the Mahalanobis distance transform (MDT) space. The similarity between MDT pairs is then computed using the cross-correlation with an asymmetric weight function based on the Mahalanobis distance. The MDCC considers correlation on cross-color channels, thus providing robustness to affine Illumination variation. Experimental results show that the MDCC outperforms state-of-the-art similarity measures in terms of stereo matching for image pairs taken under different Illumination conditions.
-
Illumination Invariant color space and its application to skin color detection
Optical Engineering, 2010Co-Authors: Ukil Yang, Bongjoe Kim, Karann Toh, Kwanghoon SohnAbstract:Color-based digital image processing (DIP) techniques have attracted much attention in many vision-based applications. However, due to color variations resulting from Illumination changes, many color-based DIP techniques have yet to demonstrate a stable state of performance. Skin-color detection, which is one of the popular color-based DIP techniques, must overcome the Illumination problems. We address the issue by presenting an Illumination-Invariant color space based on the image acquisition model that is determined by the Lambertian surface. Furthermore, we propose a method of skin-color detection based on the Illumination-Invariant color space. To evaluate the performance in terms of the Illumination-Invariant property, we perform a skin-color detection experiment. In the experiment, we compare the proposed method with the methods based on several color spaces. From the experiment, we achieve encouraging results, and our empirical experiments evidence both the effectiveness and the usefulness of the proposed method.
