Journal articles: 'Human face recognition'

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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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Chyuan Jy Wu and Jun S. Huang. "Human face profile recognition by computer." Pattern Recognition 23, no. 3-4 (January 1990): 255–59. http://dx.doi.org/10.1016/0031-3203(90)90013-b.

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Ramya, V., V. Kavitha, and P. Sivagamasundhari. "Human Face Recognition using Elman Networks." International Journal of Engineering Trends and Technology 17, no. 6 (November 25, 2014): 293–96. http://dx.doi.org/10.14445/22315381/ijett-v17p260.

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Bichsel, M. "Human Face Recognition and the Face Image Set's Topology." Computer Vision and Image Understanding 59, no. 2 (March 1994): 254–61. http://dx.doi.org/10.1006/cviu.1994.1019.

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Haan, Michelle de, Olivier Pascalis, and Mark H. Johnson. "Specialization of Neural Mechanisms Underlying Face Recognition in Human Infants." Journal of Cognitive Neuroscience 14, no. 2 (February 1, 2002): 199–209. http://dx.doi.org/10.1162/089892902317236849.

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Newborn infants respond preferentially to simple face-like patterns, raising the possibility that the face-specific regions identified in the adult cortex are functioning from birth. We sought to evaluate this hypothesis by characterizing the specificity of infants' electrocortical responses to faces in two ways: (1) comparing responses to faces of humans with those to faces of nonhuman primates; and 2) comparing responses to upright and inverted faces. Adults' face-responsive N170 event-related potential (ERP) component showed specificity to upright human faces that was not observable at any point in the ERPs of infants. A putative “infant N170” did show sensitivity to the species of the face, but the orientation of the face did not influence processing until a later stage. These findings suggest a process of gradual specialization of cortical face processing systems during postnatal development.

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Kulkarni, Narayan, and Ashok V. Sutagundar. "Detection of Human Facial Parts Using Viola-Jones Algorithm in Group of Faces." International Journal of Applied Evolutionary Computation 10, no. 1 (January 2019): 39–48. http://dx.doi.org/10.4018/ijaec.2019010103.

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Face detection is an image processing technique used in computer system to detect face in digital image. This article proposes an approach to detect faces and facial parts from an image of a group of people using the Viola Jones algorithm. Face detection is used in face recognition and identification systems. Automatic face detection and recognition is most challenging and a fast-growing research area in real-time applications like CC TV surveillance, video tracking, facial expression recognition, gesture recognition, human computer interaction, computer vision, and gender recognition. For face detection purposes various techniques and methods are applied in a computer system. In proposed system, a Viola Jones algorithm is implemented for multiple faces and facial parts and detected with a high rate of accuracy.

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CHEN, LIANG-HUA, SHAO-HUA DENG, and HONG-YUAN LIAO. "MCE-BASED FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 08 (December 2001): 1311–27. http://dx.doi.org/10.1142/s0218001401001477.

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This paper proposes a complete procedure for the extraction and recognition of human faces in complex scenes. The morphology-based face detection algorithm can locate multiple faces oriented in any direction. The recognition algorithm is based on the minimum classification error (MCE) criterion. In our work, the minimum classification error formulation is incorporated into a multilayer perceptron neural network. Experimental results show that our system is robust to noisy images and complex background.

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Sirovich, Lawrence, and Marsha Meytlis. "Symmetry, probability, and recognition in face space." Proceedings of the National Academy of Sciences 106, no. 17 (April 13, 2009): 6895–99. http://dx.doi.org/10.1073/pnas.0812680106.

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The essential midline symmetry of human faces is shown to play a key role in facial coding and recognition. This also has deep and important connections with recent explorations of the organization of primate cortex, as well as human psychophysical experiments. Evidence is presented that the dimension of face recognition space for human faces is dramatically lower than previous estimates. One result of the present development is the construction of a probability distribution in face space that produces an interesting and realistic range of (synthetic) faces. Another is a recognition algorithm that by reasonable criteria is nearly 100% accurate.

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Khalifa, Aly, Ahmed A. Abdelrahman, Dominykas Strazdas, Jan Hintz, Thorsten Hempel, and Ayoub Al-Hamadi. "Face Recognition and Tracking Framework for Human–Robot Interaction." Applied Sciences 12, no. 11 (May 30, 2022): 5568. http://dx.doi.org/10.3390/app12115568.

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Recently, face recognition became a key element in social cognition which is used in various applications including human–robot interaction (HRI), pedestrian identification, and surveillance systems. Deep convolutional neural networks (CNNs) have achieved notable progress in recognizing faces. However, achieving accurate and real-time face recognition is still a challenging problem, especially in unconstrained environments due to occlusion, lighting conditions, and the diversity in head poses. In this paper, we present a robust face recognition and tracking framework in unconstrained settings. We developed our framework based on lightweight CNNs for all face recognition stages, including face detection, alignment and feature extraction, to achieve higher accuracies in these challenging circumstances while maintaining the real-time capabilities required for HRI systems. To maintain the accuracy, a single-shot multi-level face localization in the wild (RetinaFace) is utilized for face detection, and additive angular margin loss (ArcFace) is employed for recognition. For further enhancement, we introduce a face tracking algorithm that combines the information from tracked faces with the recognized identity to use in the further frames. This tracking algorithm improves the overall processing time and accuracy. The proposed system performance is tested in real-time experiments applied in an HRI study. Our proposed framework achieves real-time capabilities with an average of 99%, 95%, and 97% precision, recall, and F-score respectively. In addition, we implemented our system as a modular ROS package that makes it straightforward for integration in different real-world HRI systems.

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M. P, Milan. "CHALLENGES IN FACE RECOGNITION TECHNIQUE." Journal of University of Shanghai for Science and Technology 23, no. 07 (July 24, 2021): 1201–4. http://dx.doi.org/10.51201/jusst/21/07253.

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Face detection is an application that is able of detecting, track, and recognizing human faces from an angle or video captured by a camera. A lot of advances have been made up in the domain of face recognition for security, identification, and appearance purpose, but still, difficult to able to beat humans alike accuracy. There are various problems in human facial presence such as; lighting conditions, image noise, scale, presentation, etc. Unconstrained face detection remains a difficult problem due to intra-class variations acquired by occlusion, disguise, capricious orientations, facial expressions, age variations…etc. The detection rate of face recognition algorithms is actually low in these conditions. With the popularity of AI in recent years, a mass number of enterprises deployed AI algorithms in absolute life settings. it is complete that face patterns observed by robots depend generally on variations such as pose, light environment, location.

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Saha, Rajib, Debotosh Bhattacharjee, and Sayan Barman. "Comparison of Different Face Recognition Method Based On PCA." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 10, no. 4 (November 4, 2014): 2016–22. http://dx.doi.org/10.24297/ijmit.v10i4.626.

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This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using: (a) Eigen faces and (b) applying Principal Component Analysis (PCA) on image. The aim is to successfully demonstrate the human face recognition using Principal component analysis & comparison of Manhattan distance, Eucleadian distance & Chebychev distance for face matching.

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Moret-Tatay, Carmen, Inmaculada Baixauli-Fortea, and M. Dolores Grau-Sevilla. "Profiles on the Orientation Discrimination Processing of Human Faces." International Journal of Environmental Research and Public Health 17, no. 16 (August 10, 2020): 5772. http://dx.doi.org/10.3390/ijerph17165772.

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Face recognition is a crucial subject for public health, as socialization is one of the main characteristics for full citizenship. However, good recognizers would be distinguished, not only by the number of faces they discriminate but also by the number of rejected stimuli as unfamiliar. When it comes to face recognition, it is important to remember that position, to some extent, would not entail a high cognitive cost, unlike other processes in similar areas of the brain. The aim of this paper was to examine participant’s recognition profiles according to face position. For this reason, a recognition task was carried out by employing the Karolinska Directed Emotional Faces. Reaction times and accuracy were employed as dependent variables and a cluster analysis was carried out. A total of two profiles were identified in participants’ performance, which differ in position in terms of reaction times but not accuracy. The results can be described as follows: first, it is possible to identify performance profiles in visual recognition of faces that differ in position in terms of reaction times, not accuracy; secondly, results suggest a bias towards the left. At the applied level, this could be of interest with a view to conducting training programs in face recognition.

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Bichsel, M., and A. P. Pentland. "Human Face Recognition and the Face Image Set′s Topology." CVGIP: Image Understanding 59, no. 2 (March 1994): 254–61. http://dx.doi.org/10.1006/ciun.1994.1017.

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Phillips, P. Jonathon, Amy N. Yates, Ying Hu, Carina A. Hahn, Eilidh Noyes, Kelsey Jackson, Jacqueline G. Cavazos, et al. "Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms." Proceedings of the National Academy of Sciences 115, no. 24 (May 29, 2018): 6171–76. http://dx.doi.org/10.1073/pnas.1721355115.

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Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.

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Zimmermann, Friederike G. S., Xiaoqian Yan, and Bruno Rossion. "An objective, sensitive and ecologically valid neural measure of rapid human individual face recognition." Royal Society Open Science 6, no. 6 (June 2019): 181904. http://dx.doi.org/10.1098/rsos.181904.

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Humans may be the only species able to rapidly and automatically recognize a familiar face identity in a crowd of unfamiliar faces, an important social skill. Here, by combining electroencephalography (EEG) and fast periodic visual stimulation (FPVS), we introduce an ecologically valid, objective and sensitive neural measure of this human individual face recognition function. Natural images of various unfamiliar faces are presented at a fast rate of 6 Hz, allowing one fixation per face, with variable natural images of a highly familiar face identity, a celebrity, appearing every seven images (0.86 Hz). Following a few minutes of stimulation, a high signal-to-noise ratio neural response reflecting the generalized discrimination of the familiar face identity from unfamiliar faces is observed over the occipito-temporal cortex at 0.86 Hz and harmonics. When face images are presented upside-down, the individual familiar face recognition response is negligible, being reduced by a factor of 5 over occipito-temporal regions. Differences in the magnitude of the individual face recognition response across different familiar face identities suggest that factors such as exposure, within-person variability and distinctiveness mediate this response. Our findings of a biological marker for fast and automatic recognition of individual familiar faces with ecological stimuli open an avenue for understanding this function, its development and neural basis in neurotypical individual brains along with its pathology. This should also have implications for the use of facial recognition measures in forensic science.

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Rifaee, Mustafa, Mohammad Al Rawajbeh, Basem AlOkosh, and Farhan AbdelFattah. "A New approach to Recognize Human Face Under Unconstrained Environment." International Journal of Advances in Soft Computing and its Applications 14, no. 2 (July 20, 2022): 2–13. http://dx.doi.org/10.15849/ijasca.220720.01.

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Human face is considered as one of the most useful traits in biometrics, and it has been widely used in education, security, military and many other applications. However, in most of currently deployed face recognition systems ideal imaging conditions are assumed; to capture a fully featured images with enough quality to perform the recognition process. As the unmasked face will have a considerable impact on the numbers of new infections in the era of COVID-19 pandemic, a new unconstrained partial facial recognition method must be developed. In this research we proposed a mask detection method based on HOG (Histogram of Gradient) features descriptor and SVM (Support Vector Machine) to determine whether the face is masked or not, the proposed method was tested over 10000 randomly selected images from Masked Face-Net database and was able to correctly classify 98.73% of the tested images. Moreover, and to extract enough features from partially occluded face images, a new geometrical features extraction algorithm based on Contourlet transform was proposed. The method achieved 97.86% recognition accuracy when tested over 4784 correctly masked face images from Masked Face-Net database. Keywords: Facial Recognition, Unconstraint conditions, masked faces, HOG, Support Vector Machine.

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KABEER, V., and N. K. NARAYANAN. "WAVELET-BASED ARTIFICIAL LIGHT RECEPTOR MODEL FOR HUMAN FACE RECOGNITION." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 05 (September 2009): 617–27. http://dx.doi.org/10.1142/s0219691309003124.

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This paper presents a novel biologically-inspired and wavelet-based model for extracting features of faces from face images. The biological knowledge about the distribution of light receptors, cones and rods, over the surface of the retina, and the way they are associated with the nerve ends for pattern vision forms the basis for the design of this model. A combination of classical wavelet decomposition and wavelet packet decomposition is used for simulating the functional model of cones and rods in pattern vision. The paper also describes the experiments performed for face recognition using the features extracted on the AT & T face database (formerly, ORL face database) containing 400 face images of 40 different individuals. In the recognition stage, we used the Artificial Neural Network Classifier. A feature vector of size 40 is formed for face images of each person and recognition accuracy is computed using the ANN classifier. Overall recognition accuracy obtained for the AT & T face database is 95.5%.

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., Dr Dolly Reney. "Review on Human face and Expression Recognition." CSVTU International Journal of Biotechnology Bioinformatics and Biomedical 3, no. 3 (February 18, 2019): 31–40. http://dx.doi.org/10.30732/ijbbb.20180303001.

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There are the different popular algorithms and techniques available which are used for implementation of face and expression recognition all having respective advantages and disadvantages. Some of the algorithms improve the efficiency of face and expression recognition, under the different varying illumination and expression conditions for input source. The main steps for face recognition are Feature representation and classification. The different authors have described different novel approaches for face and emotion recognition. Present review paper discrebie the different methods and techniques used to identified the person with the facial expression and person emotion with the voice of the person.

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Wechsler, Harry. "Linguistics and face recognition." Journal of Visual Languages & Computing 20, no. 3 (June 2009): 145–55. http://dx.doi.org/10.1016/j.jvlc.2009.01.001.

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Z.Ismaeel, Tarik, Aya A. Kamil, and Ahkam K. Naji. "Human Face Recognition using Stationary Multiwavelet Transform." International Journal of Computer Applications 72, no. 1 (June 26, 2013): 23–32. http://dx.doi.org/10.5120/12459-8813.

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Prasad, Dr R. Satya, Muzhir Shaban Al-Ani, and Salwa Mohammed Nejres. "Human Identification via Face Recognition: Comparative Study." IOSR Journal of Computer Engineering 19, no. 03 (May 2017): 17–22. http://dx.doi.org/10.9790/0661-1903021722.

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K. Naji, Saba, and Muthana H. Hamd. "HUMAN IDENTIFICATION BASED ON FACE RECOGNITION SYSTEM." Journal of Engineering and Sustainable Development 25, no. 01 (January 1, 2021): 80–91. http://dx.doi.org/10.31272/jeasd.25.1.7.

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Due to, the great electronic development, which reinforced the need to define people's identities, different methods, and databases to identification people's identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40 and 401 subjects taken from ORL and UFI databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance.

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Gupta, Priya, Nidhi Saxena, Meetika Sharma, and Jagriti Tripathi. "Deep Neural Network for Human Face Recognition." International Journal of Engineering and Manufacturing 8, no. 1 (January 8, 2018): 63–71. http://dx.doi.org/10.5815/ijem.2018.01.06.

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Zainuddin, Z., D. J. Evans, and M. H. Ahmad Fadzil. "Human Face Recognition Using Accelerated Multilayer Perceptrons." International Journal of Computer Mathematics 80, no. 5 (May 2003): 535–58. http://dx.doi.org/10.1080/0020716022000002774.

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Yang, Yang, Cong Yu, Hang Xiao, and Nan-xiang Yu. "A Study of Recognition about Human Face." Journal of Mathematics and Informatics 8 (June 21, 2017): 25–35. http://dx.doi.org/10.22457/jmi.v8a4.

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Adler, Andy, and Michael E. Schuckers. "Comparing Human and Automatic Face Recognition Performance." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37, no. 5 (October 2007): 1248–55. http://dx.doi.org/10.1109/tsmcb.2007.907036.

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Gao, Yongsheng, and Maylor K. H. Leung. "Human face profile recognition using attributed string." Pattern Recognition 35, no. 2 (February 2002): 353–60. http://dx.doi.org/10.1016/s0031-3203(01)00023-1.

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Choi, Wing-Pong, Siu-Hong Tse, Kwok-Wai Wong, and Kin-Man Lam. "Simplified Gabor wavelets for human face recognition." Pattern Recognition 41, no. 3 (March 2008): 1186–99. http://dx.doi.org/10.1016/j.patcog.2007.07.025.

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Bhattacharjee, Debotosh, Dipak K. Basu, Mita Nasipuri, and Mohantapash Kundu. "Human face recognition using fuzzy multilayer perceptron." Soft Computing 14, no. 6 (March 31, 2009): 559–70. http://dx.doi.org/10.1007/s00500-009-0426-0.

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Hussien, Abbas. "CWT and Fisherface for Human Face Recognition." International Journal of Computer Applications 142, no. 6 (May 17, 2016): 27–30. http://dx.doi.org/10.5120/ijca2016909837.

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Peng, H., and D. Zhang. "Dual eigenspace method for human face recognition." Electronics Letters 33, no. 4 (1997): 283. http://dx.doi.org/10.1049/el:19970203.

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Chaddah, Puneet, S. K. Yadav, and Ajoy Kumar Ray. "Recognition of Human Face using Interconnection Network." IETE Journal of Research 42, no. 4-5 (July 1996): 261–67. http://dx.doi.org/10.1080/03772063.1996.11415932.

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Haddadnia, Javad, and Majid Ahmadi. "N-feature neural network human face recognition." Image and Vision Computing 22, no. 12 (October 2004): 1071–82. http://dx.doi.org/10.1016/j.imavis.2004.03.011.

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Akamatsu, Shigeru. "Computer recognition of human face?A survey." Systems and Computers in Japan 30, no. 10 (September 1999): 76–89. http://dx.doi.org/10.1002/(sici)1520-684x(199909)30:10<76::aid-scj8>3.0.co;2-i.

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Wang, Zhi Wen, and Shao Zi Li. "Face Recognition Based on Template Matching and Skin-Color Segmentation." Advanced Materials Research 271-273 (July 2011): 165–70. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.165.

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In order to overcome these deficiencies that computation of recognition algorithm based on template matching is very high and the recognition rate of recognition algorithms based on skin-color segmentation is low, and is vulnerable to the impact of background which is similar with skin-color, face recognition algrithom based on skin color segmentation and template matching is presented in this paper. According to the clustering properties that the skin-color of human faces have emerged in the YCbCr color space, the regions closing to facial skin color are separated from the image by using Gaussian mixture model in order to achieve the purpose of rapidly detecting the external face of human face. Adaptive template matching is used to overcome the affect of the backgrounds which are similar with skin color on face recognition. Computation in the matching process is reduced by using the second matching algorithm. Extraction of face images by using singular value features is used to identify faces and to reduce the dimensions of the eigenvalue matrix in the course of facial feature extraction. Experimental results show that proposed method can rapidly recongnise human faces, and improve the accuracy of face recognition.

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Rahul, G. Sai. "Face Recognition based Attendance System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4448–55. http://dx.doi.org/10.22214/ijraset.2021.35859.

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Human face is one of the natural traits and crucial part of human body that can uniquely identify an individual. In the current old system the roll numbers are called out by the teachers and their presence or absence is marked accordingly which is time consuming and has a lot of ambiguity that caused inaccuracy and inefficiency of attendance marking. The productive time of the class can be utilized very efficiently by implementing automated attendance system. The main purpose of this project is to build a face recognition-based attendance monitoring system for any educational institution or organization where attendance marking is the demanding task. It enhances and upgrades the current attendance system into more efficient and effective as compared to before. This attendance system which uses HaarCascade a machine learning Object Detection Algorithm used to identify faces in an image or a real time video, Local Binary Pattern Histogram (LBPH) a face recognizer algorithm used to extract features and compare by using python programming and OpenCV libraries saves time and efficiently identifies and eliminates the chances of proxy attendance. This model integrates a camera that captures an input image and training database is created by training the system with the faces of the authorized students.

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SAMAL, ASHOK, and PRASANA A. IYENGAR. "HUMAN FACE DETECTION USING SILHOUETTES." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (December 1995): 845–67. http://dx.doi.org/10.1142/s0218001495000353.

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Face detection is integral to any automatic face recognition system. The goal of this research is to develop a system that performs the task of human face detection automatically in a scene. A system to correctly locate and identify human faces will find several applications, some examples are criminal identification and authentication in secure systems. This work presents a new approach based on principal component analysis. Face silhouettes instead of intensity images are used for this research. It results in reduction in both space and processing time. A set of basis face silhouettes are obtained using principal component analysis. These are then used with a Hough-like technique to detect faces. The results show that the approach is robust, accurate and reasonably fast.

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Yamada, Emi, Katsuya Ogata, Tomokazu Urakawa, and Shozo Tobimatsu. "S19-1. A face recognition study with morphing human face into monkey face." Clinical Neurophysiology 124, no. 8 (August 2013): e28. http://dx.doi.org/10.1016/j.clinph.2013.02.066.

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Gramigna, Remo, and Cristina Voto. "Notes on the semiotics of face recognition." Sign Systems Studies 49, no. 3-4 (December 31, 2021): 338–60. http://dx.doi.org/10.12697/sss.2021.49.3-4.05.

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Perceiving and recognizing others via their faces is of pivotal importance. The ability to perceive others in the environment – to discern between friends and foes, selves and others – as well as to detect and seek to predict their possible moves, plans, and intentions, is a set of skills that has proved to be essential in the evolutionary history of humankind. The aim of this study is to explore the subject of face recognition as a semiotic phenomenon. The scope of this inquiry is limited to face perception by the human species. The human face is analysed on the threshold between biological processes and cultural processes. We argue that the recognition of likenesses has a socio-cultural dimension that should not be overlooked. By drawing on Georg Lichtenberg’s remarks on physiognomy, we discuss the critique of the semiotic bias, the association of ideas, and the mechanism of typification involved in face recognition. Face typification is discussed against the background of face recognition and face identification. We take them as three gradients of meaning that map out a network of relationships concerning different cognitive operations that are at stake when dealing with the recognition of faces.

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Haig, Nigel D. "Exploring Recognition with Interchanged Facial Features." Perception 15, no. 3 (June 1986): 235–47. http://dx.doi.org/10.1068/p150235.

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Any attempt to unravel the mechanism underlying the process of human face recognition must begin with experiments that explore human sensitivity to differences between a perceived image and an original memory trace. A set of three consecutive experiments are reported that were collectively designed to measure the relative importance of different facial features. The method involved the use of image-processing equipment to interchange cardinal features among frontally viewed target faces. Observers were required to indicate which of the original target faces most resembled the modified faces. The results clearly establish the dominant influence of the head outline as the major recognition feature. Next in importance is the eye/eyebrow combination, followed by the mouth, and then the nose. As a recognition feature in a frontally presented face, the nose is hardly noticed. The number of apparently random responses to some faces indicates that a surprisingly different face can sometimes arise from a fortuitous combination of the old features.

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Matsuda, Yoshitaka, Kazuo Okanoya, Satoshi Hirata, and Masako Myowa-Yamakoshi. "Familiar face + novel face = familiar face? Uncanny valley? : Morphed face recognition in human and chimpanzee." Proceedings of the Annual Convention of the Japanese Psychological Association 78 (September 10, 2014): 1EV—1–073–1EV—1–073. http://dx.doi.org/10.4992/pacjpa.78.0_1ev-1-073.

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Journal articles on the topic 'Human face recognition'

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