Journal articles: 'Face identification'

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GARG, UMA. "Face Identification System-A Review." International Journal of Scientific Research 2, no. 9 (June 1, 2012): 77–78. http://dx.doi.org/10.15373/22778179/sep2013/27.

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Reddy, Mr B. Ravinder, V. Akhil, and G. Sai Preetham P. Sai Poojitha. "Profile Identification through Face Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1482–83. http://dx.doi.org/10.31142/ijtsrd23439.

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Bindemann, Markus, Rob Jenkins, and A. Mike Burton. "A Bottleneck in Face Identification." Experimental Psychology 54, no. 3 (January 2007): 192–201. http://dx.doi.org/10.1027/1618-3169.54.3.192.

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Abstract. There is evidence that face processing is capacity-limited in distractor interference tasks and in tasks requiring overt recognition memory. We examined whether capacity limits for faces can be observed with a more sensitive measure of visual processing, by measuring repetition priming of flanker faces that were presented alongside a face or a nonface target. In Experiment 1, we found identity priming for face flankers, by measuring repetition priming across a change in image, during task-relevant nonface processing, but not during the processing of a concurrently-presented face target. Experiment 2 showed perceptual priming of the flanker faces, across identical images at prime and test, when they were presented alongside a face target. In a third Experiment, all of these effects were replicated by measuring identity priming and perceptual priming within the same task. Overall, these results imply that face processing is capacity limited, such that only a single face can be identified at one time. Merely attending to a target face appears sufficient to trigger these capacity limits, thereby extinguishing identification of a second face in the display, although our results demonstrate that the additional face remains at least subject to superficial image processing.

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Robertson, David J., Rob Jenkins, and A. Mike Burton. "Face detection dissociates from face identification." Visual Cognition 25, no. 7-8 (June 2, 2017): 740–48. http://dx.doi.org/10.1080/13506285.2017.1327465.

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Parihar, Virat. "Face Identification System." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (July 31, 2020): 394–98. http://dx.doi.org/10.22214/ijraset.2020.7064.

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Shirai, Risako, and Hirokazu Ogawa. "Morality extracted under crowding impairs face identification." i-Perception 13, no. 3 (May 2022): 204166952211048. http://dx.doi.org/10.1177/20416695221104843.

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We investigated whether morality associated with faces is perceptible even under less optimal visual conditions such as crowding. A facial image was paired with a sentence describing an immoral act or a neutral act. Participants imagined the person performing the actions described in the sentence during the learning phase. Then, in the crowding phase, the target face was briefly presented in the left or right peripheral visual fields. Participants were required to judge the gender or morality of the target face in Experiment 1 and to choose the target face from two faces in Experiment 2. In both experiments, flankers were presented around the target face in the flanker condition, whereas no flankers were presented in the no-flanker condition. Experiment 1 indicated that the accuracy of judgments about the morality of a crowded face was higher for immoral faces than for neutral faces. This demonstrates that morality is preferentially extracted even when conscious access to facial representations is limited. Experiment 2 showed that the accuracy of selecting the flanked face from two faces was higher for neutral faces than for immoral faces. These indicated that the morality processed under the crowding impaired the discrimination of the facial identity.

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Cleary, Anne M., and Laura E. Specker. "Recognition without face identification." Memory & Cognition 35, no. 7 (October 2007): 1610–19. http://dx.doi.org/10.3758/bf03193495.

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Singh, Abhay Pratap. "Criminal Face Identification System." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2068–72. http://dx.doi.org/10.22214/ijraset.2020.5339.

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Lao, J., L. He, and R. Caldara. "Microsaccades Boost Face Identification." Journal of Vision 13, no. 9 (July 25, 2013): 1344. http://dx.doi.org/10.1167/13.9.1344.

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Psychology, J. R., M. Moscovitch, and M. Cadieux. "Face identification is dissociable from face imagery and generic face representation." Journal of Vision 3, no. 9 (March 18, 2010): 826. http://dx.doi.org/10.1167/3.9.826.

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Kumar, Anil, and Meenu Kumari. "Face Identification Using HAAR Wavelet Transform." International Journal of Advance Research and Innovation 8, no. 1 (2020): 8–11. http://dx.doi.org/10.51976/ijari.812002.

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Face identification is the process of matching one or more people by analyzing and comparing the patterns of their faces. Algorithms for face identification typically extract facial features and compare them to a database to find the best match. The Haar wavelet transform has been mainly used for image processing and pattern identification due to its low computing requirements and quality to conserve and to compact the energy of a signal. In discrete wavelet transform, an image signal can be analyzed by passing it through an analysis filter bank followed by a decimation operation. Face identification has been performed in terms of correlation coefficient, Euclidean distance and sum of absolute difference.

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Zhang, Zhi, Xin Xu, Jiuzhen Liang, and Bingyu Sun. "Unconstrained Face Identification Based on 3D Face Frontalization and Support Vector Guided Dictionary Learning." Mathematical Problems in Engineering 2020 (November 3, 2020): 1–16. http://dx.doi.org/10.1155/2020/8817571.

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Face identification aims at putting a label on an unknown face with respect to some training set. Unconstrained face identification is a challenging problem because of the possible variations in face pose, illumination, occlusion, and facial expression. This paper presents an unconstrained face identification method based on face frontalization and learning-based data representation. Firstly, the frontal views of unconstrained face images are automatically generated by using a single, unchanged 3D face model. Then, we crop the face relevant regions of the frontal views to segment faces from the backgrounds. At last, to enhance the discriminative capability of the coding vectors, a support vector-guided dictionary learning (SVGDL) model is applied to adaptively assign different weights to different pairs of coding vectors. The performance of the proposed method FSVGDL (frontalization-based support vector guided dictionary learning) is evaluated on the Labeled Faces in the wild (LFW) database. After decision fusion, the identification accuracy yields 97.17% when using 7 images per individual for training and 3 images per individual for testing with 158 classes in total.

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Tzschaschel, Eva, Malte Persike, and Bozana Meinhardt-Injac. "The effect of texture on face identification and configural information processing." Psihologija 47, no. 4 (2014): 433–47. http://dx.doi.org/10.2298/psi1404433t.

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Shape and texture are an integral part of face identity. In the present study, the importance of face texture for face identification and detection of configural manipulation (i.e., spatial relation among facial features) was examined by comparing grayscale face photographs (i.e., real faces) and line drawings of the same faces. Whereas real faces provide information about texture and shape of faces, line drawings are lacking texture cues. A change-detection task and a forced-choice identification task were used with both stimuli categories. Within the change detection task, participants had to decide whether the size of the eyes of two sequentially presented faces had changed or not. After having made this decision, three faces were shown to the subjects and they had to identify the previously shown face among them. Furthermore, context (full vs. cropped faces) and orientation (upright vs. inverted) were manipulated. The results obtained in the change detection task suggest that configural information was used in processing real faces, while part-based and featural information was used in processing line-drawings. Additionally, real faces were identified more accurately than line drawings, and identification was less context but more orientation sensitive than identification of line drawings. Taken together, the results of the present study provide new evidence stressing the importance of face texture for identity encoding and configural face processing.

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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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Grudzień, Artur, Marcin Kowalski, and Norbert Pałka. "Thermal Face Verification through Identification." Sensors 21, no. 9 (May 10, 2021): 3301. http://dx.doi.org/10.3390/s21093301.

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This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe that the proposed double image method can also be applied to other spectral ranges and modalities different than the face.

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Prasanna, Y. Lakshmi, U. Bhargava Lakshmi, V. Tanuja, V. Divya, and A. Prashant. "Criminal Identification through Face Recognition." International Journal of Computer Sciences and Engineering 7, no. 3 (March 31, 2019): 46–49. http://dx.doi.org/10.26438/ijcse/v7i3.4649.

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Nakajima, Masato. "Face Identification by FG Sensor." Journal of Robotics and Mechatronics 11, no. 2 (April 20, 1999): 117–22. http://dx.doi.org/10.20965/jrm.1999.p0117.

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In this paper, we propose a new method for human face identification using 3-D shading image which combines 3-D distance information with gray scaled one. This image is acquired by Fiber-grating Visual Sensor. And the method is free from the fluctuation of the direction of the face in face data acquisition, so subjects are required to stand on a footprint and reflect the nose in the square flame at the center of the mirror. We introduce the partial space method for the Identification. And Right identification rate of 96.5% is achieved as a result.

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Hani, Shimaa, Hesham Keshk, Mohamed El-Adawy, and Tarek El- Tobely. "FACE PROFILE RECOGNITION AND IDENTIFICATION." JES. Journal of Engineering Sciences 39, no. 5 (September 1, 2011): 1043–54. http://dx.doi.org/10.21608/jesaun.2011.129383.

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Noyes, Eilidh, and Rob Jenkins. "Deliberate disguise in face identification." Journal of Experimental Psychology: Applied 25, no. 2 (June 2019): 280–90. http://dx.doi.org/10.1037/xap0000213.

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Zhang, Ming Hui, and Yao Yu Zhang. "The Adaboost Algorithm Applied in ATM Automatic Identification System." Advanced Materials Research 753-755 (August 2013): 2941–44. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2941.

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Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

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Miellet, Sébastien, Roberto Caldara, and Philippe G. Schyns. "Local Jekyll and Global Hyde." Psychological Science 22, no. 12 (November 10, 2011): 1518–26. http://dx.doi.org/10.1177/0956797611424290.

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The main concern in face-processing research is to understand the processes underlying the identification of faces. In the study reported here, we addressed this issue by examining whether local or global information supports face identification. We developed a new methodology called “ iHybrid.” This technique combines two famous identities in a gaze-contingent paradigm, which simultaneously provides local, foveated information from one face and global, complementary information from a second face. Behavioral face-identification performance and eye-tracking data showed that the visual system identified faces on the basis of either local or global information depending on the location of the observer’s first fixation. In some cases, a given observer even identified the same face using local information on one trial and global information on another trial. A validation in natural viewing conditions confirmed our findings. These results clearly demonstrate that face identification is not rooted in a single, or even preferred, information-gathering strategy.

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Fachrurrozi, Muhammad, Anggina Primanita, Rafly Pakomgan, and Abdiansah Abdiansah. "Real-Time Occluded Face Identification Using Deep Learning." JURNAL TEKNIK INFORMATIKA 16, no. 1 (May 28, 2023): 69–79. http://dx.doi.org/10.15408/jti.v16i1.31211.

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One of the most difficult aspects of face identification is face occlusion. Face occlusion is when anything is placed over the face, for example, a mask. Masks occlude multiple important facial features, like the chin, lips, nose, and facial edges. Face identification becomes challenging when important facial features are occluded. Using one of the deep learning algorithms, YOLOv5, this work tries to identify the face of someone whose face is occluded by a mask in real-time. A special program is being created to test the effectiveness of the YOLOv5 algorithm. 14 people's data were registered, and each person had 150 images used for training, validation, and testing. The images used are regular faces and mask-occluded faces. Nine distinct configurations of epoch and batch sizes were used to train the model. Then, during the testing phase, the best-performing configuration was chosen. Images and real-time input were used for testing. The highest possible accuracy of image identification is 100%, whereas the maximum accuracy of real-time identification is 64%. It was found during the testing that the brightness of the room has an influence on the performance of YOLOv5. Identifying individuals becomes more challenging when there are significant changes in brightness.

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Pachai, Matthew V., Patrick J. Bennett, and Allison B. Sekuler. "The Bandwidth of Diagnostic Horizontal Structure for Face Identification." Perception 47, no. 4 (January 19, 2018): 397–413. http://dx.doi.org/10.1177/0301006618754479.

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Horizontally oriented spatial frequency components are a diagnostic source of face identity information, and sensitivity to this information predicts upright identification accuracy and the magnitude of the face-inversion effect. However, the bandwidth at which this information is conveyed, and the extent to which human tuning matches this distribution of information, has yet to be characterized. We designed a 10-alternative forced choice face identification task in which upright or inverted faces were filtered to retain horizontal or vertical structure. We systematically varied the bandwidth of these filters in 10° steps and replaced the orientation components that were removed from the target face with components from the average of all possible faces. This manipulation created patterns that looked like faces but contained diagnostic information in orientation bands unknown to the observer on any given trial. Further, we quantified human performance relative to the actual information content of our face stimuli using an ideal observer with perfect knowledge of the diagnostic band. We found that the most diagnostic information for face identification is conveyed by a narrow band of orientations along the horizontal meridian, whereas human observers use information from a wide range of orientations.

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Zhang, Ming Hui, and Yao Yu Zhang. "Face Detection Methods Applied in ATM Automatic Identification System." Applied Mechanics and Materials 347-350 (August 2013): 3416–18. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3416.

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Seeing that there are some unsafe factors in the process of ATM using,an ATM automatic identification system with extend functions was developed. As human face features are unique, face detection is added to the ATM as a method of authentication. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

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Raden Andy Kurniawan and Umar Zaky. "RADIO FREQUENCY IDENTIFICATION AND IMAGE-BASED FACIAL IDENTIFICATION AS AN EMPLOYEE ATTENDANCE SYSTEM." International Journal of Engineering Technology and Natural Sciences 2, no. 1 (December 7, 2020): 18–26. http://dx.doi.org/10.46923/ijets.v2i1.67.

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The current development of microcontroller technology can be used to build a presence system for employees. The employee attendance system uses radio frequency identification and facial identification which is designed and built to make it easier to do attendance data recording, so that the data obtained can be precise and accurate. Data collection techniques, namely by interview and observation. The application development process uses the PHP and Python programming languages with Visual Studio Code software applications, Arduino Uno, MySQL software as a database server, and XAMPP as a support. The input used in this system is the employee's personal data and the results of employee face data retrieval which are stored in the .jpg format. The faces taken were taken from 4 people where each face was taken 20 face samples. The results are in the form of web and applications that will provide solutions to existing problems. The conclusion of this application makes it easy to do the recording and attendance, and minimize the fraud committed by employees. Retrieval of face data was taken as much as 20 data with the highest level of accuracy was 87% when the presence test was carried out.

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Głomb, K. "Does Eyewitness Guess or Recognize? Bootstrapping Face and Object Identification Accuracy." Psychology and Law 10, no. 3 (2020): 73–85. http://dx.doi.org/10.17759/psylaw.2020100306.

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The purpose of the study is to determine whether the eyewitness identification can be regarded as a reliable source of information in a police investigation. In light of the many cases of eyewitness misidentifications, it seems reasonable to determine not only what class of objects is more likely to be actually recognized, but also is the level of accuracy sufficient enough to be a solid base for an investigation or a court case. To answer the questions a two-step experiment was designed and performed. At the first stage of the study, 71 participants watched a short video clip, and a week later they were asked to identify persons and the objects that appeared in the film. The participants’ rate of face identification success was 55%, while in the case of objects it was only 28%. Bootstrap estimation was used to determine if those numbers differ from random, and as a result whether they should be considered as a result of an accidental hit. The analysis showed that in the case of objects identification the success rate is within the bounds of randomness, while face identification exceeds it. It can be concluded that unlike faces, objects are more likely guessed than recognized.

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Burns, Virginie, Guillaume Lalonde-Beaudoin, Justin Duncan, Stéphanie Bouchard, Caroline Blais, and Daniel Fiset. "Individual differences in face identification correlate with face detection ability." Journal of Vision 18, no. 10 (September 1, 2018): 929. http://dx.doi.org/10.1167/18.10.929.

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Cheng, Zhiyi, Xiatian Zhu, and Shaogang Gong. "Face re-identification challenge: Are face recognition models good enough?" Pattern Recognition 107 (November 2020): 107422. http://dx.doi.org/10.1016/j.patcog.2020.107422.

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Konar, Yaroslav, Patrick J. Bennett, and Allison B. Sekuler. "Effects of aging on face identification and holistic face processing." Vision Research 88 (August 2013): 38–46. http://dx.doi.org/10.1016/j.visres.2013.06.003.

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Tao, Shao, A. L. Ananda, and Mun Choon Chan. "Greedy face routing with face identification support in wireless networks." Computer Networks 54, no. 18 (December 2010): 3431–48. http://dx.doi.org/10.1016/j.comnet.2010.07.004.

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Hasson, Uri, Galia Avidan, Leon Y. Deouell, Shlomo Bentin, and Rafael Malach. "Face-selective Activation in a Congenital Prosopagnosic Subject." Journal of Cognitive Neuroscience 15, no. 3 (April 1, 2003): 419–31. http://dx.doi.org/10.1162/089892903321593135.

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Congenital prosopagnosia is a severe impairment in face identification manifested from early childhood in the absence of any evident brain lesion. In this study, we used fMRI to compare the brain activity elicited by faces in a congenital prosopagnosic subject (YT) relative to a control group of 12 subjects in an attempt to shed more light on the nature of the brain mechanisms subserving face identification. The face-related activation pattern of YT in the ventral occipito-temporal cortex was similar to that observed in the control group on several parameters: anatomical location, activation profiles, and hemispheric laterality. In addition, using a modified vase – face illusion, we found that YT's brain activity in the face-related regions manifested global grouping processes. However, subtle differences in the degree of selectivity between objects and faces were observed in the lateral occipital cortex. These data suggest that face-related activation in the ventral occipito-temporal cortex, although necessary, might not be sufficient by itself for normal face identification.

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Alghaili, Mohammed, Zhiyong Li, and Hamdi A. R. Ali. "FaceFilter: Face Identification with Deep Learning and Filter Algorithm." Scientific Programming 2020 (August 1, 2020): 1–9. http://dx.doi.org/10.1155/2020/7846264.

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Although significant advances have been made recently in the field of face recognition, these have some limitations, especially when faces are in different poses or have different levels of illumination, or when the face is blurred. In this study, we present a system that can directly identify an individual under all conditions by extracting the most important features and using them to identify a person. Our method uses a deep convolutional network that is trained to extract the most important features. A filter is then used to select the most significant of these features by finding features greater than zero, storing their indices, and comparing the features of other identities with the same indices as the original image. Finally, the selected features of each identity in the dataset are subtracted from features of the original image to find the minimum number that refers to that identity. This method gives good results, as we only extract the most important features using the filter to recognize the face in different poses. We achieve state-of-the-art face recognition performance using only half of the 128 bytes per face. The system has an accuracy of 99.7% on the Labeled Faces in the Wild dataset and 94.02% on YouTube Faces DB.

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Stevanov, Zorica, and Suncica Zdravkovic. "Identification based on facial parts." Psihologija 40, no. 1 (2007): 37–56. http://dx.doi.org/10.2298/psi0701037s.

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Two opposing views dominate face identification literature, one suggesting that the face is processed as a whole and another suggesting analysis based on parts. Our research tried to establish which of these two is the dominant strategy and our results fell in the direction of analysis based on parts. The faces were covered with a mask and the participants were uncovering different parts, one at the time, in an attempt to identify a person. Already at the level of a single facial feature, such as mouth or eye and top of the nose, some observers were capable to establish the identity of a familiar face. Identification is exceptionally successful when a small assembly of facial parts is visible, such as eye, eyebrow and the top of the nose. Some facial parts are not very informative on their own but do enhance recognition when given as a part of such an assembly. Novel finding here is importance of the top of the nose for the face identification. Additionally observers have a preference toward the left side of the face. Typically subjects view the elements in the following order: left eye, left eyebrow, right eye, lips, region between the eyes, right eyebrow, region between the eyebrows, left check, right cheek. When observers are not in a position to see eyes, eyebrows or top of the nose, they go for lips first and then region between the eyebrows, region between the eyes, left check, right cheek and finally chin.

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Li, Baijia. "The current situation and potential development of face recognition." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 308–16. http://dx.doi.org/10.54254/2755-2721/4/20230478.

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Face recognition has received more attention in the recent past. It refers to using biometric technology to identify individuals from a captured image by comparing it to the images in the database. There are three face recognition techniques: 2D, 2D-3D and 3D. Face recognition occurs in three processes. Firstly, face recognition begins with face detection, where an image is identified as having a face. That is followed by face extraction, which involves identifying the various faces within an image. The final stage is face classification which entails face verification or face identification. Depending on the type of system, face recognition can either occur in verification or identification mode. Additionally, face recognition has various applications in the current global environment. Face recognition can be used in security systems, hospitals, schools, and retail industries. It allows easier verification and identification of individuals. However, despite the development of the technology, there are still some challenges, such as plastic surgery, illumination, aging, occlusion and pose variation.

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Manjula, V. S. "ANALYSIS OF HUMAN FACE RECOGNITION ALGORITHM USING PCA+FDIT IN IMAGE DATABASE FOR CRIME INVESTIGATION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 3 (April 30, 2013): 788–96. http://dx.doi.org/10.24297/ijct.v4i3.4201.

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In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification Â which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting. Â Â Thus the proposed technique Â Â is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.Â Â It is very useful in identify a single person from a group of faces. Â Â Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier. Â Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.

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Srikanth, G., Adurti Swarnalatha, Thalari Abhishek, Ravula Sai Akhil Patel, and Thalari Swamy. "Missing Person Identification using Machine Learning with Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1264–66. http://dx.doi.org/10.22214/ijraset.2022.47564.

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Abstract: With advances in computing and telecommunications technologies, digital images and video are playing key roles in the present information era. This system uses powerful python algorithm through which the detection and recognition of face is very easy and efficient. Human face is an important biometric object in image and video databases of surveillance systems. Detecting and locating human faces and facial features in an image or image sequence are important tasks in dynamic environments, such as videos, where noise conditions, illuminations, locations of subjects and pose can vary significantly from frame to frame. we want to identify the person based on face data base which we have already created in own data. After that we want to start identification of face using face recognition package. Finally, we will do comparison with data base and we will say weather that person is missing person or unknown person.

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Mahmoud, Waleed Ameen, Ali Ibrahim Abbas, and Nuha Abdul Sahib Alwan. "FACE IDENTIFICATION USING BACK-PROPAGATION ADAPTIVE MULTIWAVENET." Journal of Engineering 18, no. 03 (July 21, 2023): 392–402. http://dx.doi.org/10.31026/j.eng.2012.03.12.

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Face Identification is an important research topic in the field of computer vision and pattern recognition and has become a very active research area in recent decades. Recently multiwavelet-based neural networks (multiwavenets) have been used for function approximation and recognition, but to our best knowledge it has not been used for face Identification. This paper presents a novel approach for the Identification of human faces using Back-Propagation Adaptive Multiwavenet. The proposed multiwavenet has a structure similar to a multilayer perceptron (MLP) neural network with three layers, but the activation function of hidden layer is replaced with multiscaling functions. In experiments performed on the ORL face database it achieved a recognition rate of 97.75% in the presence of facial expression, lighting and pose variations. Results are compared with its wavelet-based counterpart where it obtained a recognition rate of 10.4%. The proposed multiwavenet demonstrated very good recognition rate in the presence of variations in facial expression, lighting and pose and outperformed its wavelet-based counterpart.

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Ghouzali, Sanaa, and Souad Larabi. "Face Identification based Bio-Inspired Algorithms." International Arab Journal of Information Technology 17, no. 1 (January 1, 2019): 118–27. http://dx.doi.org/10.34028/iajit/17/1/14.

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Most biometric identification applications suffer from the curse of dimensionality as the database size becomes very large, which could negatively affect both the identification performance and speed. In this paper, we use Projection Pursuit (PP) methods to determine clusters of individuals. Support Vector Machine (SVM) classifiers are then applied on each cluster of users separately. PP clustering is conducted using Friedman and Kurtosis projection indices optimized by Genetic Algorithm and Particle Swarm Optimization methods. Experimental results obtained using YALE face database showed improvement in the performance and speed of face identification system

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Khidher, Israa, and Thamir Abdul Hafidh. "Biometrics Identification based Face Image Authentication." JOURNAL OF EDUCATION AND SCIENCE 22, no. 3 (September 1, 2009): 61–74. http://dx.doi.org/10.33899/edusj.2009.57761.

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Ammour, Basma, Larbi Boubchir, Toufik Bouden, and Messaoud Ramdani. "Face–Iris Multimodal Biometric Identification System." Electronics 9, no. 1 (January 1, 2020): 85. http://dx.doi.org/10.3390/electronics9010085.

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Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

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Sae-bae, Napa, and Utharn Buranasaksee. "Sample selection for Face Identification System." International Journal on Electrical Engineering and Informatics 14, no. 2 (June 30, 2022): 392–410. http://dx.doi.org/10.15676/ijeei.2022.14.2.9.

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., S. Adebayo Daramola. "EFFECTIVE FACE FEATURE FOR HUMAN IDENTIFICATION." International Journal of Research in Engineering and Technology 03, no. 04 (April 25, 2014): 117–20. http://dx.doi.org/10.15623/ijret.2014.0304021.

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Yakel, Deborah A., and Lawrence D. Rosenblum. "Face identification using visual speech information." Journal of the Acoustical Society of America 100, no. 4 (October 1996): 2570. http://dx.doi.org/10.1121/1.417401.

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Deshmukh, Arti, and Pund M.A. "User Identification Using Face Recognition System." International Journal of Computer Trends and Technology 67, no. 5 (May 25, 2019): 155–58. http://dx.doi.org/10.14445/22312803/ijctt-v67i5p127.

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Tsank, Yuliy, and Miguel Eckstein. "Fixation sequence consistency during face identification." Journal of Vision 16, no. 12 (September 1, 2016): 69. http://dx.doi.org/10.1167/16.12.69.

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Beveridge, J. Ross, David Bolme, Bruce A. Draper, and Marcio Teixeira. "The CSU Face Identification Evaluation System." Machine Vision and Applications 16, no. 2 (February 2005): 128–38. http://dx.doi.org/10.1007/s00138-004-0144-7.

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Samaria, Ferdinando, and Steve Young. "HMM-based architecture for face identification." Image and Vision Computing 12, no. 8 (October 1994): 537–43. http://dx.doi.org/10.1016/0262-8856(94)90007-8.

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Al-Mubarak, Haitham F. "Human Face Identification from Profile Projection." Journal of Al-Nahrain University Science 17, no. 3 (September 1, 2017): 204–9. http://dx.doi.org/10.22401/jnus.17.3.28.

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Park, Se Hyun, Jeong Tak Ryu, Byung Hyun Moon, and Kyung Ae Cha. "Unattended Reception Robot using Face Identification." Journal of the Korea Industrial Information System Society 19, no. 5 (October 30, 2014): 33–37. http://dx.doi.org/10.9723/jksiis.2014.19.5.033.

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Schwartz, W. R., Huimin Guo, Jonghyun Choi, and L. S. Davis. "Face Identification Using Large Feature Sets." IEEE Transactions on Image Processing 21, no. 4 (April 2012): 2245–55. http://dx.doi.org/10.1109/tip.2011.2176951.

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Journal articles on the topic 'Face identification'

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