Journal articles: 'Face Recognition Across Pose'

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Zhang, Xiaozheng, and Yongsheng Gao. "Face recognition across pose: A review." Pattern Recognition 42, no. 11 (November 2009): 2876–96. http://dx.doi.org/10.1016/j.patcog.2009.04.017.

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Cui, Jinrong. "Bidirectional representation for face recognition across pose." Neural Computing and Applications 23, no. 5 (August 15, 2012): 1437–42. http://dx.doi.org/10.1007/s00521-012-1093-0.

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Wang, Jinghua, Jane You, Qin Li, and Yong Xu. "Orthogonal discriminant vector for face recognition across pose." Pattern Recognition 45, no. 12 (December 2012): 4069–79. http://dx.doi.org/10.1016/j.patcog.2012.04.012.

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Shahdi, Seyed Omid, and S. A. R. Abu-Bakar. "Neural Network-Based Approach for Face Recognition Across Varying Pose." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 08 (November 22, 2015): 1556015. http://dx.doi.org/10.1142/s0218001415560157.

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At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

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Zhang, Zhenduo, Yongru Chen, Wenming Yang, Guijin Wang, and Qingmin Liao. "Pose-Invariant Face Recognition via Adaptive Angular Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3390–98. http://dx.doi.org/10.1609/aaai.v36i3.20249.

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Pose-invariant face recognition is a practically useful but challenging task. This paper introduces a novel method to learn pose-invariant feature representation without normalizing profile faces to frontal ones or learning disentangled features. We first design a novel strategy to learn pose-invariant feature embeddings by distilling the angular knowledge of frontal faces extracted by teacher network to student network, which enables the handling of faces with large pose variations. In this way, the features of faces across variant poses can cluster compactly for the same person to create a pose-invariant face representation. Secondly, we propose a Pose-Adaptive Angular Distillation loss to mitigate the negative effect of uneven distribution of face poses in the training dataset to pay more attention to the samples with large pose variations. Extensive experiments on two challenging benchmarks (IJB-A and CFP-FP) show that our approach consistently outperforms the existing methods.

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SHAH, Jamal Hussain, Muhammad SHARIF, Mudassar RAZA, and Aisha AZEEM. "Face recognition across pose variation and the 3S problem." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 22 (2014): 1423–36. http://dx.doi.org/10.3906/elk-1108-70.

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Li, Shaoxin, Xin Liu, Xiujuan Chai, Haihong Zhang, Shihong Lao, and Shiguang Shan. "Maximal Likelihood Correspondence Estimation for Face Recognition Across Pose." IEEE Transactions on Image Processing 23, no. 10 (October 2014): 4587–600. http://dx.doi.org/10.1109/tip.2014.2351265.

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Tai, Ying, Jian Yang, Lei Luo, and Jianjun Qian. "Kernel orthogonal Procrustes regression for face recognition across pose." Neurocomputing 239 (May 2017): 122–29. http://dx.doi.org/10.1016/j.neucom.2017.02.010.

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Saquib Sarfraz, M., and Olaf Hellwich. "Probabilistic learning for fully automatic face recognition across pose." Image and Vision Computing 28, no. 5 (May 2010): 744–53. http://dx.doi.org/10.1016/j.imavis.2009.07.008.

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Gross, Cornelia, and Gudrun Schwarzer. "Face recognition across varying poses in 7- and 9-month-old infants: The role of facial expression." International Journal of Behavioral Development 34, no. 5 (June 3, 2010): 417–26. http://dx.doi.org/10.1177/0165025409350364.

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Three studies were conducted to determine whether 7- and 9-month-old infants generalize face identity to a novel pose of the same face when only internal face sections with and without an emotional expression were presented. In Study 1, 7- and 9-month-old infants were habituated to a full frontal or three-quarter pose of a face with neutral facial expression. In Study 2, 7-month-olds were habituated to a face with a positive or negative expression. In the novelty preference test, immediately following habituation, infants were shown a pair of faces: the habituation face in a novel pose and a novel face in the same pose. Generalization of facial identity was inferred from longer fixation time to the novel face. Whereas 7-month-old infants did not dishabituate to the novel face with neutral expression, 9-month-olds fixated longer on the novel face with neutral expression (Study 1). However, when faces displayed a positive or negative expression 7-month-olds also looked longer at the novel face, indicating generalization of the habituation face to a novel pose (Study 2). Study 3 showed that 7-montholds’ generalization ability in Study 2 cannot be explained by an inability to discriminate between the two poses of the habituation face. Results showed 9- but not 7-month-olds recognized neutral looking faces in a novel pose, and 7-month-olds’ face recognition ability was enhanced by emotional facial expression.

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Prince, S. J. D., J. Warrell, J. H. Elder, and F. M. Felisberti. "Tied Factor Analysis for Face Recognition across Large Pose Differences." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 6 (June 2008): 970–84. http://dx.doi.org/10.1109/tpami.2008.48.

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Punnappurath, Abhijith, Ambasamudram Narayanan Rajagopalan, Sima Taheri, Rama Chellappa, and Guna Seetharaman. "Face Recognition Across Non-Uniform Motion Blur, Illumination, and Pose." IEEE Transactions on Image Processing 24, no. 7 (July 2015): 2067–82. http://dx.doi.org/10.1109/tip.2015.2412379.

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Mudunuri, Sivaram Prasad, and Soma Biswas. "Low Resolution Face Recognition Across Variations in Pose and Illumination." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 5 (May 1, 2016): 1034–40. http://dx.doi.org/10.1109/tpami.2015.2469282.

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Abbas, Hawraa H., Bilal Z. Ahmed, and Ahmed Kamil Abbas. "3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms." Cybernetics and Information Technologies 19, no. 2 (June 1, 2019): 28–37. http://dx.doi.org/10.2478/cait-2019-0013.

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Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.

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K, Haripriya, Ramya Lakshmi V., Rajeswari S, Rama T, and Vinothini K.R. "RECOGNITION OF MULTI-VIEW HUMAN FACES BASED ON MACHINE INTELLIGENCE USING KLT ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 3 (March 31, 2017): 123–34. http://dx.doi.org/10.29121/granthaalayah.v5.i3.2017.1759.

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Nowadays Image Processing has become a proficient domain due to the prolific techniques like face detection and face recognition. They play an important role in our society due to their use in wide range of applications such as surveillance, security, banking, and multimedia. One of major challenges faced in this technique of face recognition is difficulty in handling arbitrary pose variations in three dimensional representations. In video retrieval system, many approaches have been developed for recognition across pose variations and to assume the face poses to be known. These constraints made it semi-automatic. In this paper we propose a fully automatic method for multi-view face recognition of improving the accuracy or efficiency using local binary patterns. It uses tree-based data structure to create sub-grids. In this system we use KLT algorithm to detect and extract features automatically by using Eigen vectors and estimation of hessian value.

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Ping-Han Lee, Gee-Sern Hsu, Yun-Wen Wang, and Yi-Ping Hung. "Subject-Specific and Pose-Oriented Facial Features for Face Recognition Across Poses." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42, no. 5 (October 2012): 1357–68. http://dx.doi.org/10.1109/tsmcb.2012.2191773.

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Castillo, C. D., and D. W. Jacobs. "Using Stereo Matching with General Epipolar Geometry for 2D Face Recognition across Pose." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 12 (December 2009): 2298–304. http://dx.doi.org/10.1109/tpami.2009.123.

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Zhang, Xiaozheng, and Yongsheng Gao. "Heterogeneous Specular and Diffuse 3-D Surface Approximation for Face Recognition Across Pose." IEEE Transactions on Information Forensics and Security 7, no. 2 (April 2012): 506–17. http://dx.doi.org/10.1109/tifs.2011.2170068.

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Jaiswal, Ajay, Nitin Kumar, and R. K. Agrawal. "Local Linear Regression on Hybrid Eigenfaces for Pose Invariant Face Recognition." International Journal of Computer Vision and Image Processing 2, no. 2 (April 2012): 48–58. http://dx.doi.org/10.4018/ijcvip.2012040104.

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Pose variation leads to significant decline in the performance of the face recognition systems. In this paper, the authors propose a new approach HLLR, based on conjunction of hybrid-eigenfaces and local linear regression (LLR), to perform face recognition across pose. In this approach, LLR on hybrid-eigenfaces is used to generate virtual views. These virtual views in frontal and non-frontal poses are obtained using frontal gallery image. The performance of the proposed approach is compared for classification accuracy with another efficient method based on global linear regression on hybrid eigenface (HGLR). They also investigate the effect of number of images used to construct hybrid-eigenfaces on classification accuracy. Experimental results on two well known publicly available face databases demonstrate the effectiveness of the proposed approach. The suitability of proposed approach is also noticed when the number of available images is small.

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Vimal, Chamandeep. "Face Detection’s Various Techniques and Approaches: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 839–43. http://dx.doi.org/10.22214/ijraset.2022.39890.

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Abstract: In the past few years, face recognition owned significant consideration and is appreciated as one of the most promising applications in the field of image analysis. Verification and Identification have become a significant issue in the present computerized world. Various variabilities are present across human faces such as pose, expression, position and orientation, skin colour, the presence of glasses or facial hair, variations in camera gain, lighting conditions, and image resolution, because of these variabilities face detection is very complicated. In this paper, several existing face detection methods and strategies are analyzed and studied. The main goal of this paper is to present or suggest an approach that is an excellent choice for face detection. Keywords: Face detection, Recognition, CPU, Multiple layer

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Rahef Nuiaa, Riyadh, Seif Ali Abdulhussein, and Bahaa Kareem Mohammed. "GFRecog: a Generic Framework with Significant Feature Selection Approach for Face Recognition." International Journal of Engineering & Technology 7, no. 3.20 (September 1, 2018): 475. http://dx.doi.org/10.14419/ijet.v7i3.20.20592.

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Identification of Humans uniquely is given paramount importance in the contemporary world. It is evident in applications of all fields so as to ensure secure and accurate transactions. Out of many approaches biometric approach became a dependable mechanism for this purpose. Face is one of the biometrics that plays vital role in recognizing humans across the globe. Many approaches came into existence for face recognition. In this paper we proposed a generic framework known as GFRecog that is extendable to support future methods of face recognition as well. We propose a methodology for face recognition using Gabor wavelets by extracting significant features from training dataset and perform matching operation with the given input image. Projection of face images onto a feature space that reflects diversity of face images is considered an efficient approach. Our approach works with faces that are captured under different lighting conditions, expression and pose. We built a prototype application using MATLAB with a benchmark dataset to demonstrate the proof of concept. The empirical results revealed that the accuracy of the proposed face recognition method is significantly high.

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Budiharto, Widodo. "Online Training for Face Recognition System Using Improved PCA." ComTech: Computer, Mathematics and Engineering Applications 2, no. 2 (December 1, 2011): 1303. http://dx.doi.org/10.21512/comtech.v2i2.2952.

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The variation in illumination is one of the main challenging problem for face recognition. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals. Recognizing face reliably across changes in pose and illumination using PCA has proved to be a much harder problem because eigenfaces method comparing the intensity of the pixel. To solve this problem, this research proposes an online face recognition system using improved PCA for a service robot in indoor environment based on stereo vision. Tested images are improved by generating random values for varying the intensity of face images. A program for online training is also developed where the tested images are captured real-time from camera. Varying illumination in tested images will increase the accuracy using ITS face database which its accuracy is 95.5 %, higher than ATT face database’s as 95.4% and Indian face database’s as 72%. The results from this experiment are still evaluated to be improved in the future.

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Križaj, Janez, Peter Peer, Vitomir Štruc, and Simon Dobrišek. "Simultaneous multi-descent regression and feature learning for facial landmarking in depth images." Neural Computing and Applications 32, no. 24 (October 23, 2019): 17909–26. http://dx.doi.org/10.1007/s00521-019-04529-7.

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AbstractFace alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking, and animation to higher-level classification problems such as face, expression, or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance variability caused by significant pose variations. Our method builds on recent cascaded regression-based methods to facial landmarking and uses a gating mechanism to incorporate multiple linear cascaded regression models each trained for a limited range of poses into a single powerful landmarking model capable of processing arbitrary-posed input data. We develop two distinct approaches around the proposed gating mechanism: (1) the first uses a gated multiple ridge descent mechanism in conjunction with established (hand-crafted) histogram of gradients features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses and (2) the second simultaneously learns multiple-descent directions as well as binary features that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing. We evaluate both approaches in rigorous experiments on several popular datasets of 3D face images, i.e., the FRGCv2 and Bosphorus 3D face datasets and image collections F and G from the University of Notre Dame. The results of our evaluation show that both approaches compare favorably to the state-of-the-art, while exhibiting considerable robustness to pose variations.

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Nanni, Loris, Sheryl Brahnam, Alessandra Lumini, and Andrea Loreggia. "Coupling RetinaFace and Depth Information to Filter False Positives." Applied Sciences 13, no. 5 (February 25, 2023): 2987. http://dx.doi.org/10.3390/app13052987.

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Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and an analysis of human behavior. The problem is challenging because of the large variations in facial appearance across different individuals and lighting and pose conditions. One way to detect faces is to utilize a highly advanced face detection method, such as RetinaFace or YOLOv7, which uses deep learning techniques to achieve high accuracy in various datasets. However, even the best face detectors can produce false positives, which can lead to incorrect or unreliable results. In this paper, we propose a method for reducing false positives in face detection by using information from a depth map. A depth map is a two-dimensional representation of the distance of objects in an image from the camera. By using the depth information, the proposed method is able to better differentiate between true faces and false positives. The method proposed by the authors is tested on a dataset of 549 images, which includes 614 upright frontal faces. The outcomes of the evaluation demonstrate that the method effectively minimizes false positives without compromising the overall detection rate. These findings suggest that incorporating depth information can enhance the accuracy of face detection.

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Balas, Benjamin, Adam Sandford, and Kay Ritchie. "Not the norm: Face likeness is not the same as similarity to familiar face prototypes." i-Perception 14, no. 3 (May 2023): 204166952311713. http://dx.doi.org/10.1177/20416695231171355.

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Face images depicting the same individual can differ substantially from one another. Ecological variation in pose, expression, lighting, and other sources of appearance variability complicates the recognition and matching of unfamiliar faces, but acquired familiarity leads to the ability to cope with these challenges. Among the many ways that face of the same individual can vary, some images are judged to be better likenesses of familiar individuals than others. Simply put, these images look more like the individual under consideration than others. But what does it mean for an image to be a better likeness than another? Does likeness entail typicality, or is it something distinct from this? We examined the relationship between the likeness of face images and the similarity of those images to average images of target individuals using a set of famous faces selected for reciprocal familiarity/unfamiliarity across US and UK participants. We found that though likeness judgments are correlated with similarity-to-prototype judgments made by both familiar and unfamiliar participants, this correlation was smaller than the correlation between similarity judgments made by different participant groups. This implies that while familiarity weakens the relationship between likeness and similarity-to-prototype judgments, it does not change similarity-to-prototype judgments to the same degree.

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Mao, Li, Delei Zhang, Youming Chen, Tao Zhang, and Xiaoning Song. "Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection." International Journal of Advanced Robotic Systems 17, no. 6 (November 1, 2020): 172988142096757. http://dx.doi.org/10.1177/1729881420967577.

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Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.

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Ko, Hyunwoong, Kisun Kim, Minju Bae, Myo-Geong Seo, Gieun Nam, Seho Park, Soowon Park, Jungjoon Ihm, and Jun-Young Lee. "Changes in Computer-Analyzed Facial Expressions with Age." Sensors 21, no. 14 (July 16, 2021): 4858. http://dx.doi.org/10.3390/s21144858.

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Facial expressions are well known to change with age, but the quantitative properties of facial aging remain unclear. In the present study, we investigated the differences in the intensity of facial expressions between older (n = 56) and younger adults (n = 113). In laboratory experiments, the posed facial expressions of the participants were obtained based on six basic emotions and neutral facial expression stimuli, and the intensities of their faces were analyzed using a computer vision tool, OpenFace software. Our results showed that the older adults expressed strong expressions for some negative emotions and neutral faces. Furthermore, when making facial expressions, older adults used more face muscles than younger adults across the emotions. These results may help to understand the characteristics of facial expressions in aging and can provide empirical evidence for other fields regarding facial recognition.

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Russel, Newlin Shebiah, S. Arivazhagan, S. G. Amrith, and S. Adarsh. "Person Re-Identification by Siamese Network." Inteligencia Artificial 26, no. 71 (March 13, 2023): 25–33. http://dx.doi.org/10.4114/intartif.vol26iss71pp25-33.

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Re-Identification of person aims at retrieval of person across multiple non overlapping camera. There was a huge gain in the computer vision community with the advancement of deep learning features and also the number of surveillance in videos increased. The challenges faced by person re-identification is low resolution images, pose variation etc., and convolutional neural networks are supported by a number of state-of-the-art algorithms for person re-identification. In this paper, Siamese network is used to predict the similarity or dissimilarity of a person across two cameras. It's a neural architecture that takes as input a pair of images or videos and the output as the prediction of similar and dissimilar persons along with their prediction scores. The experimentation is done by using datasets iLIDS-VID, PRID 2011 and obtained a recognition accuracy of 79.52% and 85.82% respectively.

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Purkey, David, Marisa Escobar Arias, Vishal Mehta, Laura Forni, Nicholas Depsky, David Yates, and Walter Stevenson. "A Philosophical Justification for a Novel Analysis-Supported, Stakeholder-Driven Participatory Process for Water Resources Planning and Decision Making." Water 10, no. 8 (July 31, 2018): 1009. http://dx.doi.org/10.3390/w10081009.

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Two trends currently shape water resources planning and decision making: reliance on participatory stakeholder processes to evaluate water management options; and growing recognition that deterministic approaches to the evaluation of options may not be appropriate. These trends pose questions regarding the proper role of information, analysis, and expertise in the inherently social and political process of negotiating agreements and implementing interventions in the water sector. The question of how one might discover the best option in the face of deep uncertainty is compelling. The question of whether the best option even exists to be discovered is more vexing. While such existential questions are not common in the water management community, they are not new to political theory. This paper explores early classical writing related to issues of knowledge and governance as captured in the work of Plato and Aristotle; and then attempts to place a novel, analysis-supported, stakeholder-driven water resources planning and decision making practice within this philosophical discourse, making reference to current decision theory. Examples from the Andes and California, where this practice has been used to structure participation by key stakeholders in water management planning and decision-making, argue that when a sufficiently diverse group of stakeholders is engaged in the decision making process expecting the discovery of the perfect option may not be warranted. Simply discovering a consensus option may be more realistic. The argument touches upon the diversity of preferences, model credibility and the visualization of model output required to explore the implications of various management options across a broad range of inherently unknowable future conditions.

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Du, Shan, and Rabab Ward. "Face recognition under pose variations." Journal of the Franklin Institute 343, no. 6 (September 2006): 596–613. http://dx.doi.org/10.1016/j.jfranklin.2006.08.006.

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Liu Ding, Xiaoqing Ding, and Chi Fang. "Continuous Pose Normalization for Pose-Robust Face Recognition." IEEE Signal Processing Letters 19, no. 11 (November 2012): 721–24. http://dx.doi.org/10.1109/lsp.2012.2215586.

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Zhang, Yi, Keren Fu, Cong Han, Peng Cheng, Shanmin Yang, and Xiao Yang. "PGM-face: Pose-guided margin loss for cross-pose face recognition." Neurocomputing 460 (October 2021): 154–65. http://dx.doi.org/10.1016/j.neucom.2021.07.006.

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He, Mingjie, Jie Zhang, Shiguang Shan, Meina Kan, and Xilin Chen. "Deformable face net for pose invariant face recognition." Pattern Recognition 100 (April 2020): 107113. http://dx.doi.org/10.1016/j.patcog.2019.107113.

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Liu, Yanfei, and Junhua Chen. "Unsupervised face Frontalization for pose-invariant face recognition." Image and Vision Computing 106 (February 2021): 104093. http://dx.doi.org/10.1016/j.imavis.2020.104093.

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Liang, Yan, Yun Zhang, and Xian-Xian Zeng. "Pose-invariant 3D face recognition using half face." Signal Processing: Image Communication 57 (September 2017): 84–90. http://dx.doi.org/10.1016/j.image.2017.05.004.

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Changxing Ding, Chang Xu, and Dacheng Tao. "Multi-Task Pose-Invariant Face Recognition." IEEE Transactions on Image Processing 24, no. 3 (March 2015): 980–93. http://dx.doi.org/10.1109/tip.2015.2390959.

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Logie, Robert H., Alan D. Baddeley, and Muriel M. Woodhead. "Face recognition, pose and ecological validity." Applied Cognitive Psychology 1, no. 1 (January 1987): 53–69. http://dx.doi.org/10.1002/acp.2350010108.

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An, Zhanfu, Weihong Deng, Jiani Hu, Yaoyao Zhong, and Yuying Zhao. "APA: Adaptive Pose Alignment for Pose-Invariant Face Recognition." IEEE Access 7 (2019): 14653–70. http://dx.doi.org/10.1109/access.2019.2894162.

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Liu, Jinjin, Qingbao Li, Ming Liu, and Tongxin Wei. "CP-GAN: A Cross-Pose Profile Face Frontalization Boosting Pose-Invariant Face Recognition." IEEE Access 8 (2020): 198659–67. http://dx.doi.org/10.1109/access.2020.3033675.

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Firdausy, Kartika, and Balza Achmad. "Automatic Frontal Face Pose Tracking for Face Recognition System." International Journal on Advanced Science, Engineering and Information Technology 1, no. 4 (2011): 399. http://dx.doi.org/10.18517/ijaseit.1.4.1.

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Li, Pengyu, Biao Wang, and Lei Zhang. "Adversarial Pose Regression Network for Pose-Invariant Face Recognitions." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 1940–48. http://dx.doi.org/10.1609/aaai.v35i3.16289.

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Face recognition has achieved significant progress in recent years. However, the large pose variation between face images remains a challenge in face recognition. We observe that the pose variation in the hidden feature maps is one of the most critical factors to hinder the representations from being pose-invariant. Based on the observation, we propose an Adversarial Pose Regression Network (APRN) to extract pose-invariant identity representations by disentangling their pose variation in hidden feature maps. To model the pose discriminator in APRN as a regression task in its 3D space, we also propose an Adversarial Regression Loss Function and extend the adversarial learning from classification problems to regression problems in this paper. Our APRN is a plug-and-play structure that can be embedded in other state-of-the-art face recognition algorithms to improve their performance additionally. The experiments show that the proposed APRN consistently and significantly boosts the performance of baseline networks without extra computational costs in the inference phase. APRN achieves comparable or even superior to the state-of-the-art on CFP, Multi-PIE, IJB-A and MegaFace datasets. The code will be released, hoping to nourish our proposals to other computer vision fields

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Aly, Saleh, Alaa Sagheer, Naoyuki Tsuruta, and Rin-ichiro Taniguchi. "Face recognition across illumination." Artificial Life and Robotics 12, no. 1-2 (March 2008): 33–37. http://dx.doi.org/10.1007/s10015-007-0437-9.

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Gunawan, Alexander Agung Santoso, and Reza A. Prasetyo. "Face Recognition Performance in Facing Pose Variation." CommIT (Communication and Information Technology) Journal 11, no. 1 (August 1, 2017): 1. http://dx.doi.org/10.21512/commit.v11i1.1847.

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There are many real world applications of face recognition which require good performance in uncontrolled environments such as social networking, and environment surveillance. However, many researches of face recognition are done in controlled situations. Compared to the controlled environments, face recognition in uncontrolled environments comprise more variation, for example in the pose, light intensity, and expression. Therefore, face recognition in uncontrolled conditions is more challenging than in controlled settings. In thisresearch, we would like to discuss handling pose variations in face recognition. We address the representation issue us ing multi-pose of face detection based on yaw angle movement of the head as extensions of the existing frontal face recognition by using Principal Component Analysis (PCA). Then, the matching issue is solved by using Euclidean distance. This combination is known as Eigenfaces method. The experiment is done with different yaw angles and different threshold values to get the optimal results. The experimental results show that: (i) the more pose variation of face images used as training data is, the better recognition results are, but it also increases the processing time, and (ii) the lower threshold value is, the harder it recognizes a face image, but it also increases the accuracy.

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De Vel, O., and S. Aeberhard. "Line-based face recognition under varying pose." IEEE Transactions on Pattern Analysis and Machine Intelligence 21, no. 10 (1999): 1081–88. http://dx.doi.org/10.1109/34.799912.

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WI, NOEL TAY NUO, CHU KIONG LOO, and LETCHUMANAN CHOCKALINGAM. "BIOLOGICALLY INSPIRED FACE RECOGNITION: TOWARD POSE-INVARIANCE." International Journal of Neural Systems 22, no. 06 (November 27, 2012): 1250029. http://dx.doi.org/10.1142/s0129065712500293.

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A small change in image will cause a dramatic change in signals. Visual system is required to be able to ignore these changes, yet specific enough to perform recognition. This work intends to provide biological-backed insights into 2D translation and scaling invariance and 3D pose-invariance without imposing strain on memory and with biological justification. The model can be divided into lower and higher visual stages. Lower visual stage models the visual pathway from retina to the striate cortex (V1), whereas the modeling of higher visual stage is mainly based on current psychophysical evidences.

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Zhang, Haichao, Yanning Zhang, and Thomas S. Huang. "Pose-robust face recognition via sparse representation." Pattern Recognition 46, no. 5 (May 2013): 1511–21. http://dx.doi.org/10.1016/j.patcog.2012.10.025.

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Aksasse, Brahim, Hamid Ouanan, and Mohammed Ouanan. "Novel approach to pose invariant face recognition." Procedia Computer Science 110 (2017): 434–39. http://dx.doi.org/10.1016/j.procs.2017.06.108.

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Nair, Binu Muraleedharan, Jacob Foytik, Richard Tompkins, Yakov Diskin, Theus Aspiras, and Vijayan Asari. "Multi-Pose Face Recognition And Tracking System." Procedia Computer Science 6 (2011): 381–86. http://dx.doi.org/10.1016/j.procs.2011.08.070.

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Su, Ya. "Robust Video Face Recognition Under Pose Variation." Neural Processing Letters 47, no. 1 (June 13, 2017): 277–91. http://dx.doi.org/10.1007/s11063-017-9649-8.

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Zhang, Shuai, Hai Rui Wang, and Xiao Li He. "Pose-Invariant Face Synthesis and Recognition via Sparse Coding and Symmetrical Information." Applied Mechanics and Materials 437 (October 2013): 894–900. http://dx.doi.org/10.4028/www.scientific.net/amm.437.894.

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Pose variation which brings illumination change, occlusion and non-linear scale variation, dramatically drops the performance of face recognition systems. In this paper, we propose a novel pose invariant face recognition method, in which we build a joint sparse coding scheme to predict face images from a certain pose to another. By introducing autoregressive regularization and symmetric information, our algorithm could achieve high robustness to local misalignment and large pose differences. Besides, we propose a new coarse pose estimation algorithm by collaborative representation classifier, which is very fast and enough accurate for our synthesis algorithm. The experiment results on multi-pose subsets of CMU-PIE and FERET database show the efficiency of the proposed method on multi-pose face recognition.

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Journal articles on the topic 'Face Recognition Across Pose'

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