- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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3D Shape Analysis (Fundamentals, Theory, and Applications) || 3D Face Recognition
摘要： The automatic recognition of human faces has many potential applications in various fields including security and human–computer interaction. An accurate and robust face recognition system needs to discriminate between the faces of different people under variable conditions. The main challenge is that faces, from a general perspective, look similar and their differences can be very subtle. They all have the same structure and are composed of similar components (e.g. nose, eyes, and mouth). On the other hand, the appearance of the same face can considerably change under variable extrinsic factors, e.g. the camera position and the intensity and direction of light, and intrinsic factors such as the head position and orientation, facial expressions, age, skin color, and gender. On that basis, face recognition can be considered to be more challenging than the general object recognition problem discussed in Chapter 11. Pioneer researchers initially focused on 2D face recognition, i.e. how to recognize faces from data captured using monocular cameras. They reported promising recognition results, particularly in controlled environments. With the recent popularity of cost-effective 3D acquisition systems, face recognition systems are starting to benefit from the availability, advantages, and widespread use of 3D data. In this chapter, we review some of the recent advances in 3D face recognition. We will first present, in Section 10.2, the various 3D facial datasets and benchmarks that are currently available to researchers and then discuss the challenges and evaluation criteria. Section 10.3 will review the key 3D face recognition methods. Section 10.4 provides a summary and discussions around this chapter.
关键词： local feature-based matching,face identification,face verification,holistic approaches,challenges,3D face recognition,datasets
更新于2025-09-23 15:22:29
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - FACE - Face At Classroom Environment: Dataset and Exploration
摘要： The rapid development in face detection study has been greatly supported by the availability of large image datasets, which provide detailed annotations of faces on images. However, among a number of publicly accessible datasets, to our best knowledge, none of them are specifically created for academic applications. In this paper, we propose a systematic method in forming an image dataset tailored for classroom environment. We also made our dataset and its exploratory analyses publicly available. Studies in computer vision for academic application, such as an automated student attendance system, would benefit from our dataset.
关键词： image dataset,face recognition,face detection,computer vision,data collection,educational data mining,automated attendance system
更新于2025-09-23 15:22:29
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Evaluating Feature Extractors and Dimension Reduction Methods for Near Infrared Face Recognition Systems
摘要： This study evaluates the performance of global and local feature extractors as well as dimension reduction methods in NIR domain. Zernike moments (ZMs), Independent Component Analysis (ICA), Radon Transform + Discrete Cosine Transform (RDCT), Radon Transform + Discrete Wavelet Transform (RDWT) are employed as global feature extractors and Local Binary Pattern (LBP), Gabor Wavelets (GW), Discrete Wavelet Transform (DWT) and Undecimated Discrete Wavelet Transform (UDWT) are used as local feature extractors. For evaluation of dimension reduction methods Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPDA), Linear Discriminant Analysis + Principal Component Analysis (Fisherface), Kernel Fisher Discriminant Analysis (KFD) and Spectral Regression Discriminant Analysis (SRDA) are used. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that ZMs as a global feature extractor, UDWT as a local feature extractor and SRDA as a dimension reduction method have superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments.
关键词： comparative study,undecimated discrete wavelet transform,Face recognition,near infrared,Zernike moments
更新于2025-09-23 15:22:29
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Gestalt Interest Points with a Neural Network for Makeup-Robust Face Recognition
摘要： In this paper, we propose a novel approach for the domain of makeup-robust face recognition. Most face recognition schemes usually fail to generalize well on these data where there is a large difference between the training and testing sets, e.g., makeup changes. Our method focuses on the problem of determining whether face images before and after makeup refer to the same identity. The work on this fundamental research topic benefits various real-world applications, for example automated passport control, security in general, and surveillance. Experiments show that our method is highly effective in comparison to state-of-the-art methods.
关键词： CNN,Face recognition,makeup-robust,GIP,person identification
更新于2025-09-23 15:21:01
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Reconstructing 3D Face Models by Incremental Aggregation and Refinement of Depth Frames
摘要： Face recognition from two-dimensional (2D) still images and videos is quite successful even with “in the wild” conditions. Instead, less consolidated results are available for the cases in which face data come from non-conventional cameras, such as infrared or depth. In this article, we investigate this latter scenario assuming that a low-resolution depth camera is used to perform face recognition in an uncooperative context. To this end, we propose, first, to automatically select a set of frames from the depth sequence of the camera because they provide a good view of the face in terms of pose and distance. Then, we design a progressive refinement approach to reconstruct a higher-resolution model from the selected low-resolution frames. This process accounts for the anisotropic error of the existing points in the current 3D model and the points in a newly acquired frame so that the refinement step can progressively adjust the point positions in the model using a Kalman-like estimation. The quality of the reconstructed model is evaluated by considering the error between the reconstructed models and their corresponding high-resolution scans used as ground truth. In addition, we performed face recognition using the reconstructed models as probes against a gallery of reconstructed models and a gallery with high-resolution scans. The obtained results confirm the possibility to effectively use the reconstructed models for the face recognition task.
关键词： anisotropic error,3D reconstruction,3D face recognition,Depth data
更新于2025-09-19 17:15:36
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[IEEE 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) - Paris, France (2019.9.1-2019.9.6)] 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) - A Gap Waveguide Fed Circular Polarization Antenna in the Millimeter-Wave Range
摘要： Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many machine learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal with con?icting concerns of the small sample size and high dimensionality in one-sample face recognition is critical for its achievable recognition accuracy and feasibility in practice. Being different from the conventional methods for global face recognition based on generalization ability promotion and local face recognition depending on image segmentation, a single-sample face recognition algorithm based on locality preserving projection (LPP) feature transfer is proposed here. First, transfer sources are screened to obtain the selective sample source using the whitened cosine similarity metric. Second, we project the vectors of source faces and target faces into feature subspace by LPP, respectively, and calculate the feature transfer matrix to approximate the mapping relationship on source faces and target faces in subspace. Then, the feature transfer matrix is used on training samples to transfer the original macro characteristics to target macro characteristics. Finally, the nearest neighbor classi?er is used for face recognition. Our results based on popular databases FERET, ORL, and Yale demonstrate the superiority of the proposed LPP feature transfer-based one-sample face recognition algorithm when compared with popular single-sample face recognition algorithms, such as (PC)2A and Block FLDA.
关键词： one-sample,Feature extraction,face recognition,locality preserving projection,transfer learning
更新于2025-09-19 17:13:59
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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Degenerate Energy Exchange between Optical TE <sub/>2</sub> -modes of the Planar Waveguide Based on a Thin Left-handed Film and a Nonlinear Substrate
摘要： Face recognition (FR) systems in real-world applications need to deal with a wide range of interferences, such as occlusions and disguises in face images. Compared with other forms of interferences such as nonuniform illumination and pose changes, face with occlusions has not attracted enough attention yet. A novel approach, coined dynamic image-to-class warping (DICW), is proposed in this work to deal with this challenge in FR. The face consists of the forehead, eyes, nose, mouth, and chin in a natural order and this order does not change despite occlusions. Thus, a face image is partitioned into patches, which are then concatenated in the raster scan order to form an ordered sequence. Considering this order information, DICW computes the image-to-class distance between a query face and those of an enrolled subject by finding the optimal alignment between the query sequence and all sequences of that subject along both the time dimension and within-class dimension. Unlike most existing methods, our method is able to deal with occlusions which exist in both gallery and probe images. Extensive experiments on public face databases with various types of occlusions have confirmed the effectiveness of the proposed method.
关键词： image-to-class distance,Face recognition,biometrics,dynamic time warping,occlusion
更新于2025-09-19 17:13:59
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Face recognition using depth and infrared pictures
摘要： This paper proposes the face recognition method using both the depth and infrared pictures. The conventional face recognition methods based on color picture recognize the human faces accurately, but are easily a?ected by the illumination and are vulnerable to the attempts to steal user’s face information through fake face such as the photograph or the sculpture. On the other hand, the methods based on the depth or infrared picture are less a?ected by the illumination, and prevent an attempt to recognize a false face. This paper utilizes the depth picture to reduce the recognition time and the infrared picture to increase the recognition performance. In the face detection, this paper ?nds the nose of a person using the captured depth picture for reducing detection time, and it detects the region of the face. In the feature extraction, it extracts the feature pictures from the infrared picture by 3D Local Binary Pattern. In the face identi?cation, this paper compares between the features of the captured face and features of faces that are pre-stored in the DB, and obtains the face similarity. If the similarity of the face is larger than the certain threshold, the face recognition succeeds. Simulation results show that the face recognition performance is good not only in the normal environment but also in the little illumination.
关键词： local binary pattern,depth picture,face recognition,infrared picture
更新于2025-09-12 10:27:22
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[IEEE 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS) - Edinburgh, United Kingdom (2018.8.6-2018.8.9)] 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS) - Towards the design of smart video-surveillance system
摘要： Security and monitoring systems are increasingly demanding in terms of quality, reliability and ?exibility especially those dedicated to video surveillance. The aim of this study is to identify some limiting factors in the existing video-surveillance systems and to propose a set of best practices for developing a smart platform for a security monitoring system incorporating advanced techniques for video processing and analysis. In this work, we focus on the effect of the video quality on the biometric part of the video-surveillance systems for public security. In such systems, face detection and recognition from video sequences acquired from surveillance cameras, are challenging tasks, due to presence of strong illumination variations, noise, and changes in facial expressions. In this paper, we mainly focus on the illumination issue occurred in video surveillance. The low light video data is processed using a perceptual based approach, namely multi-scale Retinex method, to improve the video quality, followed by face detection. The experimental results demonstrate signi?cant performance improvement in face detection and recognition, by improving the illumination of video sequences over the unprocessed video data.
关键词： Retinex,face recognition,face detection,video surveillance,enhancement
更新于2025-09-11 14:15:04
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Adaptive illumination normalization via adaptive illumination preprocessing and modified weber-face
摘要： Illumination processing is a challenging task in face recognition. This paper proposes a novel illumination normalization method that aims to remove illumination boundaries and improve image quality under dark conditions. Firstly, to improve the image quality, an adaptive illumination preprocessing algorithm is adopted. Then we modify the Weber-Face model by suppressing the components which are greatly affected by the illumination. Experimental results on both Extended Yale B and CMU-PIE databases show that the proposed method can obtain high performance under complex illumination conditions. The accuracy on the Extended Yale B database is 93.02% and on the CMU-PIE database is 70.44%, which is the highest among the similar approaches. This method not only greatly improves the face recognition rate but also keep the computational complexity in low compared with several state-of-the-art methods.
关键词： Illumination processing,Face recognition,Illumination boundaries,Dark conditions,Weber-Face
更新于2025-09-10 09:29:36