A Multi-Face Challenging Dataset for Robust Face Recognition
01 Nov 2018-pp 168-173
TL;DR: The IIITS_MFace dataset as discussed by the authors contains faces with challenges like pose variation, occlusion, mask, spectacle, expressions, change of illumination, etc., and compare with the existing benchmark face datasets.
Abstract: Face recognition in images is an active area of interest among the computer vision researchers. However, recognizing human face in an unconstrained environment, is a relatively less-explored area of research. Multiple face recognition in unconstrained environment is a challenging task, due to the variation of view-point, scale, pose, illumination and expression of the face images. Partial occlusion of faces makes the recognition task even more challenging. The contribution of this paper is two-folds: introducing a challenging multi-face dataset (i.e., IIITS_MFace Dataset) for face recognition in unconstrained environment and evaluating the performance of state-of-the-art hand-designed and deep learning based face descriptors on the dataset. The proposed IIITS_MFace dataset contains faces with challenges like pose variation, occlusion, mask, spectacle, expressions, change of illumination, etc. We experiment with several state-of-the-art face descriptors, including recent deep learning based face descriptors like VGGFace, and compare with the existing benchmark face datasets. Results of the experiments clearly show that the difficulty level of the proposed dataset is much higher compared to the benchmark datasets.
Citations
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TL;DR: Wang et al. as mentioned in this paper proposed a method of soft tissue surface feature tracking based on a depth matching network, which is described based on the triangular matching algorithm, and the experimental results show that the proposed method has higher matching accuracy.
Abstract: Research in the field of medical image is an important part of the medical robot to operate human organs. A medical robot is the intersection of multi-disciplinary research fields, in which medical image is an important direction and has achieved fruitful results. In this paper, a method of soft tissue surface feature tracking based on a depth matching network is proposed. This method is described based on the triangular matching algorithm. First, we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm. The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set. After the training, the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame. From this matrix, the inter-frame feature matching results can be obtained. In this way, the inter-frame speckle tracking is completed. On this basis, the results of this method are compared with the matching results based on the convolutional neural network. The experimental results show that the proposed method has higher matching accuracy. In particular, the accuracy of the MNIST handwritten data set has reached more than 90%.
14 citations
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TL;DR: This paper collects and sets up a dataset called ILL-Dataset specifically for different illumination conditions and provides the detected dataset which is processed by using the face detection method and the experiment results indicate the I LL- Dataset is a challenge dataset on illumination face recognition.
Abstract: Face recognition has achieved extraordinary success recently due to the advancement of algorithms, technology, and hardware. Also, this is partly due to the richness of the face dataset. However, face recognition in complicated environment such as illumination, occlusion, pose is still a challenging task. Among them, illumination problems are still tough challenges in the field of face recognition. The datasets play an important role in deep learning, while there are few datasets specially for different illumination conditions and the currently existing face data sets have limitations. In this paper, we collect and set up a dataset called ILL-Dataset specifically for different illumination conditions. We also provide the detected dataset which is processed by using the face detection method. After it established, it can provide image data and promote the face recognition accuracy under various illumination conditions. The experiment results indicate the ILL-Dataset is a challenge dataset on illumination face recognition.
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TL;DR: Face Detection is one of the kinds of bio-metric strategies that immediately apply to facial recognition by computerized devices through staring at the facial. as discussed by the authors The best biometric information processes is a face recognition device, its applicability is simpler and its working range is broader than other methods like fingerprinting, iris scanning and signature.
Abstract: —The best biometric information processes is a face recognition device, its applicability is simpler and its working range is broader than other methods like fingerprinting, iris scanning and signature. Face Detection is one of the kinds of bio-metric strategies that immediately apply to facial recognition by computerized devices through staring at the facial. It is a common feature used in bio analytics, digital cameras, and social labeling. Main applications of facial recognition algorithms that concentrate on recognition of face include environments, artifacts, and other parts of humans. Face-detection systems uses learning algorithms which are part of machine learning that can be used to identify subject faces inside big size pictures in order to function.
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13 Oct 2022
TL;DR: In this article , the authors have found that various deep learning techniques has got success in recognizing the face while wearing a mask but still there remains an area of improvising, they also found that deep learning technique has got good performance in recognizing face.
Abstract: The objective of this is to find various researches being done for detection of hidden face while there is any occlusion and another objective is to find best deep learning technique used till date for finding the face of a person who might be involved in certain criminal activities. Due to the impact of coronavirus world has shattered so wearing a face mask is mandatory action as a prevention from the disease but people are covering their face to do certain offences. It is found that various deep learning techniques has got success in recognizing the face while wearing a mask but still there remains an area of improvising.
References
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TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Abstract: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.
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TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
Abstract: Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.
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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
5,237 citations
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TL;DR: It is shown how a very large scale dataset can be assembled by a combination of automation and human in the loop, and the trade off between data purity and time is discussed.
Abstract: The goal of this paper is face recognition – from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets. We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve comparable state of the art results on the standard LFW and YTF face benchmarks.
4,347 citations
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TL;DR: This work presents a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition, and improves robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources.
Abstract: Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three main contributions: 1) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; 2) we introduce local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions, and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and 3) we further improve robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources-Gabor wavelets and LBP-showing that the combination is considerably more accurate than either feature set alone. The resulting method provides state-of-the-art performance on three data sets that are widely used for testing recognition under difficult illumination conditions: Extended Yale-B, CAS-PEAL-R1, and Face Recognition Grand Challenge version 2 experiment 4 (FRGC-204). For example, on the challenging FRGC-204 data set it halves the error rate relative to previously published methods, achieving a face verification rate of 88.1% at 0.1% false accept rate. Further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets, data sets and lighting conditions.
2,736 citations
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