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Relevant bibliographies by topics / Human face recognition

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Academic literature on the topic 'Human face recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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Journal articles on the topic "Human face recognition"

1

Sabharwal, Himani, and Akash Tayal. "Human Face Recognition." International Journal of Computer Applications 104, no. 11 (October 18, 2014): 1–3. http://dx.doi.org/10.5120/18243-9173.

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S.G, Rajeshwari. "Human Face Recognition." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (June 30, 2020): 638–43. http://dx.doi.org/10.22214/ijraset.2020.6104.

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Choudhury, Debesh. "Three-dimensional human face recognition." Journal of Optics 38, no. 1 (March 2009): 16–21. http://dx.doi.org/10.1007/s12596-009-0002-0.

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Rossion, B. "Neurophysiology of human face recognition." Neurophysiologie Clinique 49, no. 4 (September 2019): 345. http://dx.doi.org/10.1016/j.neucli.2019.07.010.

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Shyam, Radhey, and Yogendra Narain Singh. "Multialgorithmic Frameworks for Human Face Recognition." Journal of Electrical and Computer Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4645971.

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This paper presents a critical evaluation of multialgorithmic face recognition systems for human authentication in unconstrained environment. We propose different frameworks of multialgorithmic face recognition system combining holistic and texture methods. Our aim is to combine the uncorrelated methods of the face recognition that supplement each other and to produce a comprehensive representation of the biometric cue to achieve optimum recognition performance. The multialgorithmic frameworks are designed to combine different face recognition methods such as (i) Eigenfaces and local binary pattern (LBP), (ii) Fisherfaces and LBP, (iii) Eigenfaces and augmented local binary pattern (A-LBP), and (iv) Fisherfaces and A-LBP. The matching scores of these multialgorithmic frameworks are processed using different normalization techniques whereas their performance is evaluated using different fusion strategies. The robustness of proposed multialgorithmic frameworks of face recognition system is tested on publicly available databases, for example, AT & T (ORL) and Labeled Faces in the Wild (LFW). The experimental results show a significant improvement in recognition accuracies of the proposed frameworks of face recognition system in comparison to their individual methods. In particular, the performance of the multialgorithmic frameworks combining face recognition methods with the devised face recognition method such as A-LBP improves significantly.

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Allison, T., H. Ginter, G. McCarthy, A. C. Nobre, A. Puce, M. Luby, and D. D. Spencer. "Face recognition in human extrastriate cortex." Journal of Neurophysiology 71, no. 2 (February 1, 1994): 821–25. http://dx.doi.org/10.1152/jn.1994.71.2.821.

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1. Twenty-four patients with electrodes chronically implanted on the surface of extrastriate visual cortex viewed faces, equiluminant scrambled faces, cars, scrambled cars, and butterflies. 2. A surface-negative potential, N200, was evoked by faces but not by the other categories of stimuli. N200 was recorded only from small regions of the left and right fusiform and inferior temporal gyri. Electrical stimulation of the same region frequently produced a temporary inability to name familiar faces. 3. The results suggest that discrete regions of inferior extrastriate visual cortex, varying in location between individuals, are specialized for the recognition of faces. These "face modules" appear to be intercalated among other functionally specific small regions.

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Singh, Pankaj, Sanjay Kumar Singh, and Nidhi Gaba. "YCbCr Technique based Human Face Recognition." International Journal of Advance Research and Innovation 3, no. 1 (2015): 79–83. http://dx.doi.org/10.51976/ijari.311514.

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Face detection is a necessary first-step in face recognition systems, with the reason of localize and extract the face region from the background. It also has a number of applications in areas such as content-based image recovery, video coding, video conferencing, crowd observation, and intelligent human–computer interfaces. We have taken skin color as a tool for detection. This technique works well for all types’ faces which have a specific complexion varying under definite range. We have taken real life examples and simulated the algorithms in MATLAB successfully. This paper concentrates on the input images are converted to the YCbCr model to collect the value Y,Cb,Cr. and check whether these values are satisfied with the threshold values. If the pixels are in the range of threshold then that pixels will be considered as skin region otherwise it is a non skin region. This paper defined algorithm has been tested on various real time frontal images and gets better results for the YCbCr color model.

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Bhange, Prof Anup. "Face Detection System with Face Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1095–100. http://dx.doi.org/10.22214/ijraset.2022.39976.

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Abstract: The face is one of the easiest way to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Now a days Human Face Detection and Recognition become a major field of interest in current research because there is no deterministic algorithm to find faces in a given image. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is recognition, which recognize (by comparing face with picture or either with image captured through webcam) a face as an individual. In face detection and recognition technology, it is mainly introduced from the OpenCV method. Face recognition is one of the much-studied biometrics technology and developed by experts. The area of this project face detection system with face recognition is Image processing. The software requirement for this project is Python. Keywords: face detection, face recognition, cascade_classifier, LBPH.

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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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Liu, Haisong, Minxian Wu, Guofan Jin, Gang Cheng, and Qingsheng He. "An automatic human face recognition system." Optics and Lasers in Engineering 30, no. 3-4 (September 1998): 305–14. http://dx.doi.org/10.1016/s0143-8166(98)00022-0.

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Dissertations / Theses on the topic "Human face recognition"

1

Wong, Vincent. "Human face recognition /." Online version of thesis, 1994. http://hdl.handle.net/1850/11882.

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Ener, Emrah. "Recognition Of Human Face Expressions." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/3/12607521/index.pdf.

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In this study a fully automatic and scale invariant feature extractor which does not require manual initialization or special equipment is proposed. Face location and size is extracted using skin segmentation and ellipse fitting. Extracted face region is scaled to a predefined size, later upper and lower facial templates are used for feature extraction. Template localization and template parameter calculations are carried out using Principal Component Analysis. Changes in facial feature coordinates between analyzed image and neutral expression image are used for expression classification. Performances of different classifiers are evaluated. Performance of proposed feature extractor is also tested on sample video sequences. Facial features are extracted in the first frame and KLT tracker is used for tracking the extracted features. Lost features are detected using face geometry rules and they are relocated using feature extractor. As an alternative to feature based technique an available holistic method which analyses face without partitioning is implemented. Face images are filtered using Gabor filters tuned to different scales and orientations. Filtered images are combined to form Gabor jets. Dimensionality of Gabor jets is decreased using Principal Component Analysis. Performances of different classifiers on low dimensional Gabor jets are compared. Feature based and holistic classifier performances are compared using JAFFE and AF facial expression databases.

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Batur, Aziz Umit. "Illumination-robust face recognition." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/15440.

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Zou, Weiwen. "Face recognition from video." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1431.

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Lee, Colin K. "Infrared face recognition." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FLee%5FColin.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2004.
Thesis advisor(s): Monique P. Fargues, Gamani Karunasiri. Includes bibliographical references (p. 135-136). Also available online.

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Gangam, Priyanka Reddy. "Recognizing Face Sketches by Human Volunteers." Youngstown State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1297198615.

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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." University of Sydney. Electrical and Information Engineering, 2004. http://hdl.handle.net/2123/586.

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Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.

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8

Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/586.

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Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.

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Tibbalds, Adam Dominic. "Three dimensional human face acquisition for recognition." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624854.

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Low, Boon Kee. "Computer extraction of human faces." Thesis, De Montfort University, 1999. http://hdl.handle.net/2086/10668.

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Due to the recent advances in visual communication and face recognition technologies, automatic face detection has attracted a great deal of research interest. Being a diverse problem, the development of face detection research has comprised contributions from researchers in various fields of sciences. This thesis examines the fundamentals of various face detection techniques implemented since the early 70's. Two groups of techniques are identified based on their approach in applying face knowledge as a priori: feature-based and image-based. One of the problems faced by the current feature-based techniques, is the lack of costeffective segmentation algorithms that are able to deal with issues such as background and illumination variations. As a result a novel facial feature segmentation algorithm is proposed in this thesis. The algorithm aims to combine spatial and temporal information using low cost techniques. In order to achieve this, an existing motion detection technique is analysed and implemented with a novel spatial filter, which itself is proved robust for segmentation of features in varying illumination conditions. Through spatio-temporal information fusion, the algorithm effectively addresses the background and illumination problems among several head and shoulder sequences. Comparisons of the algorithm with existing motion and spatial techniques establishes the efficacy of the combined approach.

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Books on the topic "Human face recognition"

1

Dutta, Paramartha, and Asit Barman. Human Emotion Recognition from Face Images. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3883-4.

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Handbook of face recognition. 2nd ed. London: Springer, 2011.

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Quaglia, Adamo, and Calogera M. Epifano. Face recognition: Methods, applications, and technology. Hauppauge, N.Y: Nova Science, 2012.

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Grother, Patrick J. Face recognition vendor test 2002 performance metrics. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2003.

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1948-, Wechsler Harry, North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Study Institute on Face Recognition: From Theory to Applications (1997 : Stirling, Stirling, Scotland), eds. Face recognition: From theory to applications. Berlin: Springer, 1998.

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Daijin, Kim. Automated face analysis: Emerging technologies and research. Hershey, PA: Information Science Reference, 2009.

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Uwechue, Okechukwu A. Human face recognition using third-order synthetic neural networks. Boston: Kluwer Academic Publishers, 1997.

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Uwechue, Okechukwu A. Human Face Recognition Using Third-Order Synthetic Neural Networks. Boston, MA: Springer US, 1997.

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Uwechue, Okechukwu A., and Abhijit S. Pandya. Human Face Recognition Using Third-Order Synthetic Neural Networks. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-4092-2.

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Yang, Ming-Hsuan, and Narendra Ahuja. Face Detection and Gesture Recognition for Human-Computer Interaction. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1423-7.

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Book chapters on the topic "Human face recognition"

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Bruce, Vicki, Peter J. B. Hancock, and A. Mike Burton. "Human Face Perception and Identification." In Face Recognition, 51–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_3.

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Viennet, Emmanuel, and Françoise Fogelman Soulié. "Connectionists Methods for Human Face Processing." In Face Recognition, 124–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_7.

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Schubert, A., and E. D. Dickmanns. "Real-time Gaze Observation for Tracking Human Control of Attention." In Face Recognition, 617–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_41.

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Uwechue, Okechukwu A., and Abhijit S. Pandya. "Face Recognition." In Human Face Recognition Using Third-Order Synthetic Neural Networks, 21–35. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-4092-2_2.

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Rahman, S. M. Mahbubur, Tamanna Howlader, and Dimitrios Hatzinakos. "Face Recognition." In Orthogonal Image Moments for Human-Centric Visual Pattern Recognition, 49–85. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9945-0_3.

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Wallbott, Harald G. "Recognition of Emotion in Specific Populations: Compensation, Deficit or Specific (Dis)Abilities?" In The Human Face, 169–87. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1063-5_9.

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Wang, Weihong, Jie Yang, Jianwei Xiao, Sheng Li, and Dixin Zhou. "Face Recognition Based on Deep Learning." In Human Centered Computing, 812–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15554-8_73.

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Johnston, Patrick, and Vaughan Carr. "Facial Affect Recognition Deficits in Schizophrenia: A Case for Applying Facial Measurement Techniques." In The Human Face, 119–30. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1063-5_6.

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Jiao, Yanbin, Jucheng Yang, Zhijun Fang, Shanjuan Xie, and Dongsun Park. "Comparing Studies of Learning Methods for Human Face Gender Recognition." In Biometric Recognition, 67–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35136-5_9.

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Ren, Jianfeng, Xudong Jiang, and Junsong Yuan. "Face and Facial Expressions Recognition and Analysis." In Human–Computer Interaction Series, 3–29. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19947-4_1.

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Conference papers on the topic "Human face recognition"

1

"Human and face recognition." In 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2015. http://dx.doi.org/10.1109/ipta.2015.7367141.

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Owais, Muhammad, Ammara Shaikh, Aireen Amir Jalal, and Muhammad Moiz Hassan. "Human Face Recognition using PCA Eigenfaces." In 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, 2019. http://dx.doi.org/10.1109/icetas48360.2019.9117489.

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Pike, G. "The psychology of human face recognition." In IEE Colloquium on Visual Biometrics. IEE, 2000. http://dx.doi.org/10.1049/ic:20000471.

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Ravindranath, Saurabh, Rahul Baburaj, Vineeth N. Balasubramanian, NageswaraRao Namburu, Sujit Gujar, and C. V. Jawahar. "Human-Machine Collaboration for Face Recognition." In CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3371158.3371160.

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Chenggang Zhen and Yingmei Su. "Research about human face recognition technology." In 2009 International Conference on Test and Measurement (ICTM). IEEE, 2009. http://dx.doi.org/10.1109/ictm.2009.5412901.

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Liu, Ke, Frederic Jallut, Ying-Jiang Liu, Yong-Qing Cheng, and Jingyu Yang. "Novel approach to human face recognition." In San Diego '92, edited by Andrew G. Tescher. SPIE, 1993. http://dx.doi.org/10.1117/12.139107.

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Singh, Geetika, and Indu Chhabra. "Human face recognition through moment descriptors." In 2014 Recent Advances in Engineering and Computational Sciences (RAECS). IEEE, 2014. http://dx.doi.org/10.1109/raecs.2014.6799551.

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Kamgar-Parsi, Behrooz, Wallace Edgar Lawson, and Behzad Kamgar-Parsi. "Face recognition motivated by human approach." In SPIE Defense, Security, and Sensing, edited by B. V. K. Vijaya Kumar, Salil Prabhakar, and Arun A. Ross. SPIE, 2010. http://dx.doi.org/10.1117/12.855707.

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Cai, Defu, and Mingfang Zhang. "Model matching for human face recognition." In Intelligent Systems & Advanced Manufacturing, edited by David P. Casasent. SPIE, 1997. http://dx.doi.org/10.1117/12.290301.

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Singh Tomar, Vivek, Neetesh Gupta, and Upendra Singh. "Expressions Recognition Based on Human Face." In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2019. http://dx.doi.org/10.1109/iccmc.2019.8819714.

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Reports on the topic "Human face recognition"

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Phillips, P. Jonathon, Alice J. O'Toole, Yi Cheng, Brendan Ross, and Heather A. Wild. Assessing algorithms as computational models for human face recognition. Gaithersburg, MD: National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6348.

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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmarks, while others bring out more detail. We use 68 facial markup, which is a common format for many datasets. Cloud computing creates all the necessary conditions for the successful implementation of even the most complex tasks. We created a web application using the Django framework, Python language, OpenCv and Dlib libraries to recognize faces in the image. The purpose of our work is to create a software system for face recognition in the photo and identify wrinkles on the face. The algorithm for determining the presence and location of various types of wrinkles and determining their geometric determination on the face is programmed.

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, December 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these traits, and their applications, vein pattern recognition is reviewed.

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Clarke, Alison, Sherry Hutchinson, and Ellen Weiss. Psychosocial support for children. Population Council, 2005. http://dx.doi.org/10.31899/hiv14.1003.

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Masiye Camp in Matopos National Park, and Kids’ Clubs in downtown Bulawayo, Zimbabwe, are examples of a growing number of programs in Africa and elsewhere that focus on the psychological and social needs of AIDS-affected children. Given the traumatic effects of grief, loss, and other hardships faced by these children, there is increasing recognition of the importance of programs to help them strengthen their social and emotional support systems. This Horizons Report describes findings from operations research in Zimbabwe and Rwanda that examines the psychosocial well-being of orphans and vulnerable children and ways to increase their ability to adapt and cope in the face of adversity. In these studies, a person’s psychosocial well-being refers to his/her emotional and mental state and his/her network of human relationships and connections. A total of 1,258 youth were interviewed. All were deemed vulnerable by their communities because they had been affected by HIV/AIDS and/or other factors such as severe poverty.

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Gurevitz, Michael, William A. Catterall, and Dalia Gordon. Learning from Nature How to Design Anti-insect Selective Pesticides - Clarification of the Interacting Face between Insecticidal Toxins and their Na-channel Receptors. United States Department of Agriculture, January 2010. http://dx.doi.org/10.32747/2010.7697101.bard.

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Structural details on the interacting faces of toxins and sodium channels (Navs), and particularly identification of elements that confer specificity for insects, are difficult to approach and require suitable experimental systems. Therefore, natural toxins capable of differential recognition of insect and mammalian Navs are valuable leads for design of selective compounds in insect control. We have characterized several scorpion toxins that vary in preference for insect and mammalian Navs, and identified residues important for their action. However, despite many efforts worldwide, only little is known about the receptor sites of these toxins, and particularly on differences between these sites on insect and mammalian Navs. Another problem arises from the massive overuse of chemical insecticides, which increases resistance buildup among various insect pests. A possible solution to this problem is to combine different insecticidal compounds, especially those that provide synergic effects. Our recent finding that combinations of insecticidal receptor site-3 toxins (sea anemone and scorpion alpha) with scorpion beta toxins or their truncated derivatives are synergic in toxicity to insects is therefore timely and strongly supports this approach. Our ability to produce toxins and various Navs in recombinant forms, enable thorough analysis and structural manipulations of both toxins and receptors. On this basis we propose to (1) restrict by mutagenesis the activity of insecticidal scorpion -toxins and sea anemone toxins to insects, and clarify the molecular basis of their synergic toxicity with antiinsect selective -toxins; (2) identify Nav elements that interact with scorpion alpha and sea anemone toxins and those that determine toxin selectivity to insects; (3) determine toxin-channel pairwise side-chain interactions by thermodynamic mutant cycle analysis using our large collection of mutant -toxins and Nav mutants identified in aim 2; (4) clarify the mode of interaction of truncated -toxins with insect Navs, and elucidate how they enhance the activity of insecticidal site-3 toxins. This research may lead to rational design of novel anti-insect peptidomimetics with minimal impact on human health and the environment, and will establish the grounds for a new strategy in insect pest control, whereby a combination of allosterically interacting compounds increase insecticidal action and reduce risks of resistance buildup.

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Kroll, Joshua A. ACM TechBrief: Facial Recognition Technology. ACM, February 2022. http://dx.doi.org/10.1145/3520137.

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Facial recognition is not a monolithic technology or a particular technique. Rather, facial recognition refers to any technology that automatically processes and purports to identify faces in images or videos. While humans interpret faces easily, computers must extract patterns from data or humans must code patterns into the system. Applying these patterns yields the facial descriptors (often referred to as faceprints) on which facial recognition systems rely to achieve their function.

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Gordon, Dalia, Ke Dong, and Michael Gurevitz. Unexpected Specificity of a Sea Anemone Small Toxin for Insect Na-channels and its Synergic Effects with Various Insecticidal Ligands: A New Model to Mimic. United States Department of Agriculture, November 2010. http://dx.doi.org/10.32747/2010.7697114.bard.

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Motivated by the high risks to the environment and human health imposed by the current overuse of chemical insecticides we offer an alternative approach for the design of highly active insect-selective compounds that will be based on the ability of natural toxins to differentiate between insect and mammalian targets. We wish to unravel the interacting surfaces of insect selective toxins with their receptor sites on voltage-gated sodium channels. In this proposal we put forward two recent observations that may expedite the development of a new generation of insect killers that mimic the highly selective insecticidal toxins: (i) A small (27aa) highly insecticidal sea anemone toxin, Av3, whose toxicity to mammals is negligible; (ii) The prominent positive cooperativity between distinct channel ligands, such as the strong enhancement of pyrethroids effects by anti-insect selective scorpion depressant toxins. We possess a repertoire of insecticidal toxins and sodium channel subtypes all available in recombinant form for mutagenesis followed by analysis of various pharmacological, electrophysiological, and structural methods. Our recent success to express Av3 provides for the first time a selective toxin for receptor site-3 on insect sodium channels. In parallel, our recent success to determine the structures and bioactive surfaces of insecticidal site-3 and site-4 toxins establishes a suitable system for elucidation of toxin-receptor interacting faces. This is corroborated by our recent identification of channel residues involved with these two receptor sites. Our specific aims in this proposal are to (i) Determine the bioactive surface of Av3 toward insect Na-channels; (ii) Identify channel residues involved in binding or activity of the insecticidal toxins Av3 and LqhaIT, which differ substantially in their potency on mammals; (iii) Illuminate channel residues involved in recognition by the anti-insect depressant toxins; (iv) Determine the face of interaction of both site-3 (Av3) and site-4 (LqhIT2) toxins with insect sodium channels using thermodynamic mutant cycle analysis; and, (v) Examine whether Av3, LqhIT2, pyrethroids, and indoxacarb (belongs to a new generation of insecticides), enhance allosterically the action of one another on the fruit fly and cockroach paraNa-channels and on their kdr and super-kdr mutants. This research establishes the grounds for rational design of novel anti-insect peptidomimetics with minimal impact on human health, and offers a new approach in insect pest control, whereby a combination of allosterically interacting compounds increases insecticidal action and reduces risks of resistance buildup.

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Kukreja, Prateek, Havishaye Puri, and Dil Rahut. Creative India: Tapping the Full Potential. Asian Development Bank Institute, January 2023. http://dx.doi.org/10.56506/kcbi3886.

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We provide the first reliable measure on the size of India’s creative economy, explore the many challenges faced by the creative industries, and provide recommendations to make India one of the most creative societies in the world. India’s creative economy—measured by the number of people working in various creative occupations—is estimated to contribute nearly 8% of the country’s employment, much higher than the corresponding share in Turkey (1%), Mexico (1.5%), the Republic of Korea (1.9%), and even Australia (2.1%). Creative occupations also pay reasonably well—88% higher than the non-creative ones and contribute about 20% to nation’s overall GVA. Out of the top 10 creative districts in India, 6 are non-metros—Badgam, Panipat (Haryana), Imphal (Manipur), Sant Ravi Das Nagar (Uttar Pradesh), Thane (Maharashtra), and Tirupur (Tamil Nadu)—indicating the diversity and depth of creativity across India. Yet, according to the United Nations Conference on Trade and Development, India’s creative exports are only one-tenth of those of the People’s Republic of China. To develop the creative economy to realize its full potential, Indian policy makers would like to (i) increase the recognition of Indian culture globally; (ii) facilitate human capital development among its youth; (iii) address the bottlenecks in the intellectual property framework; (iv) improve access to finance; and (v) streamline the process of policy making by establishing one intermediary organization. India must also leverage its G20 Presidency to put creative economy concretely on the global agenda.

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Issues in Data Processing and Relevant Population Selection. OSAC Speaker Recognition Subcommittee, November 2022. http://dx.doi.org/10.29325/osac.tg.0006.

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In Forensic Automatic Speaker Recognition (FASR), forensic examiners typically compare audio recordings of a speaker whose identity is in question with recordings of known speakers to assist investigators and triers of fact in a legal proceeding. The performance of automated speaker recognition (SR) systems used for this purpose depends largely on the characteristics of the speech samples being compared. Examiners must understand the requirements of specific systems in use as well as the audio characteristics that impact system performance. Mismatch conditions between the known and questioned data samples are of particular importance, but the need for, and impact of, audio pre-processing must also be understood. The data selected for use in a relevant population can also be critical to the performance of the system. This document describes issues that arise in the processing of case data and in the selections of a relevant population for purposes of conducting an examination using a human supervised automatic speaker recognition approach in a forensic context. The document is intended to comply with the Organization of Scientific Area Committees (OSAC) for Forensic Science Technical Guidance Document.

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