A Robust Face Recognition Method for Occluded and Low-Resolution Images
01 Aug 2019-
TL;DR: A near real-time and novel face recognition method to recognize the occluded and low-resolution face images and outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate.
Abstract: Face images that appear in multimedia applications, such as digital entertainments usually exhibit dramatic nonuniform illumination, occlusions, low-resolution, and pose/expression variations that result in substantial performance degradation for traditional face recognition algorithms. Recent research is focused to develop robust face recognition algorithms to solve the aforementioned issues with maximum effort to mimic the human vision system. This paper presents a near real-time and novel face recognition method to recognize the occluded and low-resolution face images. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image. Meanwhile, the adaptive boosting and Linear Discriminant Analysis (LDA) are used to extract face features. In the final stage, classic nearest centre classifier is used for face classification. Detailed experiments are performed on two publicly available LFW and the AR databases. Simulation results reveal that the proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate.
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TL;DR: A novel technique for concealed face detection based on complexion detection to challenge a concealed face assumption is presented, which achieves an average detection rate of 97.51% and a run time comparable with existing state-of-the-art concealed face Detection systems that run in real time.
Abstract: Face detection perceives great importance in surveillance paradigm and security paradigm areas. Face recognition is the technique to identify a person identity after face detection. Extensive research has been done on these topics. Another important research problem is to detect concealed faces, especially in high-security places like airports or crowded places like concerts and shopping centres, for they may prevail security threat. Also, in order to help effectively in preventing the spread of Coronavirus, people should wear masks during the pandemic especially in the entrance to hospitals and medical facilities. Surveillance systems in medical facilities should issue warnings against unmasked people. This paper presents a novel technique for concealed face detection based on complexion detection to challenge a concealed face assumption. The proposed algorithm first determine of the existence of a human being in the surveillance scene. Head and shoulder contour will be detected. The face will be clustered to cluster patches. Then determination of presence or absent of human skin will be determined. We proposed a hybrid approach that combines normalized RGB (rgb) and the YCbCr space color. This technique is tested on two datasets; the first one contains 650 images of skin patches. The second dataset contains 800 face images. The algorithm achieves an average detection rate of 97.51% for concealed faces. Also, it achieved a run time comparable with existing state-of-the-art concealed face detection systems that run in real time.
23 citations
Cites background from "A Robust Face Recognition Method fo..."
...[40] presented a real-time and new face recognition technique with occlusion....
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TL;DR: The proposed Deep Belief Network-Crossover based Firefly (DBN-CROFF) method for face recognition from low-resolution images surpasses the other conventional facial recognition approaches by giving a higher accuracy of recognization.
Abstract: Face detection by low-resolution image (LR) is one of the key aspects of Human-Computer Interaction(HCI). Due to the LR image, which has changes in pose, lighting, and illumination, the performance of face recognition is reduced. In this work, we propose the Deep Belief Network-Crossover based Firefly (DBN-CROFF) method for face recognition from low-resolution images. The Histogram of Gradient (HOG) and 2-Dimensional Discrete Wavelet Transform (2D-DWT) to extract facial width, size of the cheeks, skin tone, nose, and lip shape features from facial data. The Kernel Principle Component Analysis (k-PCA) is used to successfully reduce the dimension of the feature. The experimental performance of the proposed method is evaluated using four datasets namely LFW, Multi-PIE, Extended Yale-B, and FERET with conventional techniques. Finally, the proposed DBN-CROFF solution surpasses the other conventional facial recognition approaches by giving a higher accuracy of recognization.
5 citations
16 Dec 2020
TL;DR: In this paper, an intelligent combination of Linear Discriminant Analysis (LDA) and Adaptive Boosting is used to extract features from detected and normalized face images, and classic nearest center classifier is applied to achieve classification.
Abstract: Face Recognition (FR) has been vastly studied in the past few decades and researchers have been able to achieve success in this field, mostly in controlled environments However, the FR algorithm faces huge drop in its accuracy due to the variation in pose and expression in face images This paper proposes a novel FR method to overcome the issues of pose and expression The proposed FR algorithm initially locates faces using dual shot face detection algorithm Later, an intelligent combination of Linear Discriminant Analysis (LDA) and Adaptive Boosting is used to extract features from detected and normalized face images Finally, classic nearest center classifier is applied to achieve classification Detailed experiments are conducted on JAFFE and KDEF databases Moreover, a thorough analysis and comparison of the effect of pose and expression variation on the accuracy of FR is presented The experimental results show that the proposed algorithm achieves better recognition accuracy than the recent FR methods
4 citations
References
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01 Oct 2008
TL;DR: The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life, and exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background.
Abstract: Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.
5,742 citations
"A Robust Face Recognition Method fo..." refers methods in this paper
...Experiments were performed on the LFW [7], Extended Yale B [8], and AR [9] dataset....
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...LFW Database [7]: This database contains 13,233 images of 5749 people out of which 1680 people have at least one image....
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TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed
5,563 citations
TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.
Abstract: We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions.
5,027 citations
"A Robust Face Recognition Method fo..." refers methods in this paper
...Experiments were performed on the LFW [7], Extended Yale B [8], and AR [9] dataset....
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01 Jan 1998
3,650 citations
"A Robust Face Recognition Method fo..." refers methods in this paper
...Experiments were performed on the LFW [7], Extended Yale B [8], and AR [9] dataset....
[...]
...AR Database [9]: This database consists of over 4,000 face images of 126 subjects with 70 men and 56 females....
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...LFW Database [7]: This database contains 13,233 images of 5749 people out of which 1680 people have at least one image....
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Journal Article•
2,952 citations
"A Robust Face Recognition Method fo..." refers methods in this paper
...Experiments were performed on the LFW [7], Extended Yale B [8], and AR [9] dataset....
[...]
...AR Database [9]: This database consists of over 4,000 face images of 126 subjects with 70 men and 56 females....
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