Cyborg groups enhance face recognition in crowded environments

Valeriani, Davide; Poli, Roberto

doi:10.1371/journal.pone.0212935

Cited by 16 publications

(12 citation statements)

References 48 publications

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“…In previous studies, we focused on predicting the probability of each trial being correct, as this is particularly useful for aiding group decisions [38][39][40][49][50][51]. In this study however, we focus our attention on predicting the confidence reported by participants after each decision.…”

Section: Confidence Predictionmentioning

confidence: 99%

Subject- and task-independent neural correlates and prediction of decision confidence in perceptual decision making

et al. 2021

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Objective. In many real-world decision tasks, the information available to the decision maker is incomplete. To account for this uncertainty, we associate a degree of confidence to every decision, representing the likelihood of that decision being correct. In this study, we analyse electroencephalography (EEG) data from 68 participants undertaking eight different perceptual decision-making experiments. Our goals are to investigate (1) whether subject-and task-independent neural correlates of decision confidence exist, and ( 2) to what degree it is possible to build brain computer interfaces that can estimate confidence on a trial-by-trial basis. The experiments cover a wide range of perceptual tasks, which allowed to separate the task-related, decision-making features from the task-independent ones. Approach. Our systems train artificial neural networks to predict the confidence in each decision from EEG data and response times. We compare the decoding performance with three training approaches: (1) single subject, where both training and testing data were acquired from the same person; (2) multi-subject, where all the data pertained to the same task, but the training and testing data came from different users; and (3) multi-task, where the training and testing data came from different tasks and subjects. Finally, we validated our multi-task approach using data from two additional experiments, in which confidence was not reported. Main results. We found significant differences in the EEG data for different confidence levels in both stimulus-locked and response-locked epochs. All our approaches were able to predict the confidence between 15% and 35% better than the corresponding reference baselines. Significance. Our results suggest that confidence in perceptual decision making tasks could be reconstructed from neural signals even when using transfer learning approaches. These confidence estimates are based on the decision-making process rather than just the confidence-reporting process.

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Section: Confidence Predictionmentioning

confidence: 99%

Subject- and task-independent neural correlates and prediction of decision confidence in perceptual decision making

et al. 2021

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“…They used three datasets: AR Face, YALE and SDUM-LA-HMT [6]. Further research on FR can be found in [7][8][9][10].…”

Section: Literature Reviewmentioning

confidence: 99%

Researching a machine learning algorithm for a face recognition system

Yevseiev

Goloskokova

Shmatko

2021

TT: PhE

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This article investigated the problem of using machine learning algorithms to recognize and identify a user in a video sequence. The scientific novelty lies in the proposed improved Viola-Jones method, which will allow more efficient and faster recognition of a person's face. The practical value of the results obtained in the work is determined by the possibility of using the proposed method to create systems for human face recognition. A review of existing methods of face recognition, their main characteristics, architecture and features was carried out. Based on the study of methods and algorithms for finding faces in images, the Viola-Jones method, wavelet transform and the method of principal components were chosen. These methods are among the best in terms of the ratio of recognition efficiency and work speed. Possible modifications of the Viola-Jones method are presented. The main contribution presented in this article is an experimental study of the impact of various types of noise and the improvement of company security through the development of a computer system for recognizing and identifying users in a video sequence. During the study, the following tasks were solved: – a model of face recognition is proposed, that is, the system automatically detects a person's face in the image (scanned photos or video materials); – an algorithm for analyzing a face is proposed, that is, a representation of a person's face in the form of 68 modal points; – an algorithm for creating a digital fingerprint of a face, which converts the results of facial analysis into a digital code; – development of a match search module, that is, the module compares the faceprint with the database until a match is found

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“…They used three datasets: AR Face, YALE, and SDUMLA-HMT [19]. Further research on FR can be found in [20][21][22][23].…”

Section: Plos Onementioning

confidence: 99%

A deep facial recognition system using computational intelligent algorithms

et al. 2020

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The development of biometric applications, such as facial recognition (FR), has recently become important in smart cities. Many scientists and engineers around the world have focused on establishing increasingly robust and accurate algorithms and methods for these types of systems and their applications in everyday life. FR is developing technology with multiple real-time applications. The goal of this paper is to develop a complete FR system using transfer learning in fog computing and cloud computing. The developed system uses deep convolutional neural networks (DCNN) because of the dominant representation; there are some conditions including occlusions, expressions, illuminations, and pose, which can affect the deep FR performance. DCNN is used to extract relevant facial features. These features allow us to compare faces between them in an efficient way. The system can be trained to recognize a set of people and to learn via an online method, by integrating the new people it processes and improving its predictions on the ones it already has. The proposed recognition method was tested with different three standard machine learning algorithms (Decision Tree (DT), K Nearest Neighbor(KNN), Support Vector Machine (SVM)). The proposed system has been evaluated using three datasets of face images (SDUMLA-HMT, 113, and CASIA) via performance metrics of accuracy, precision, sensitivity, specificity, and time. The experimental results show that the proposed method achieves superiority over other algorithms according to all parameters. The suggested algorithm results in higher accuracy (99.06%), higher precision (99.12%), higher recall (99.07%), and higher specificity (99.10%) than the comparison algorithms.

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Cyborg groups enhance face recognition in crowded environments

Cited by 16 publications

References 48 publications

Subject- and task-independent neural correlates and prediction of decision confidence in perceptual decision making

Subject- and task-independent neural correlates and prediction of decision confidence in perceptual decision making

Researching a machine learning algorithm for a face recognition system

A deep facial recognition system using computational intelligent algorithms

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