Applied screening tests for the detection of superior face recognition.
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ABSTRACT:
SUBMITTER: Bate S
PROVIDER: S-EPMC6019417 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.
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Project description:Human babies respond preferentially to faces or face-like images. It has been proposed that an innate and rapid face detection system is present at birth before the cortical visual pathway is developed in many species, including primates. However, in primates, the visual area responsible for this process is yet to be unraveled. We hypothesized that the superior colliculus (SC) that receives direct and indirect retinal visual inputs may serve as an innate rapid face-detection system in primates. To test this hypothesis, we examined the responsiveness of monkey SC neurons to first-order information of faces required for face detection (basic spatial layout of facial features including eyes, nose, and mouth), by analyzing neuronal responses to line drawing images of: (1) face-like patterns with contours and properly placed facial features; (2) non-face patterns including face contours only; and (3) nonface random patterns with contours and randomly placed face features. Here, we show that SC neurons respond stronger and faster to upright and inverted face-like patterns compared to the responses to nonface patterns, regardless of contrast polarity and contour shapes. Furthermore, SC neurons with central receptive fields (RFs) were more selective to face-like patterns. In addition, the population activity of SC neurons with central RFs can discriminate face-like patterns from nonface patterns as early as 50 ms after the stimulus onset. Our results provide strong neurophysiological evidence for the involvement of the primate SC in face detection and suggest the existence of a broadly tuned template for face detection in the subcortical visual pathway.
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Project description: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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Project description:While there have been decades of clinical and theoretical interest in developmental and acquired face recognition difficulties, very little work has examined their remediation. Here, we report two studies that examined the efficacy of an existing face training programme in improving face-processing skills in adults and children with developmental face recognition impairments. The programme has only been trialled in typical children to date, where 2 weeks of perceptual training (modelled on an adapted version of the popular family game Guess Who?) resulted in face-specific improvements for memory but not perception after 2 weeks of training. In Study 1, we performed a randomized, parallel groups, placebo-controlled trial of the same programme in 20 adults with a pre-existing diagnosis of developmental prosopagnosia. Assessment tasks were administered immediately before and after training, and 2 weeks later. Face-specific gains in memory (but not perception) were observed in the experimental group and were greatest in those with the poorest face recognition skills at entry. These gains persisted 2 weeks after training ceased. In Study 2, a case-series approach was used to administer the experimental version of the training programme to four children who presented with difficulties in face recognition. Improvements in face memory were observed in three of the participants; while one also improved at face perception, there was mixed evidence for the face specificity of these gains. Together, these findings suggest plasticity in the human face recognition system through to at least mid-adulthood and also pave the way for longer-term implementations of the face training programme that will likely elicit greater gains in both adults and children.
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Project description:BackgroundEmerging blood-based multi-cancer early detection (MCED) tests can detect a variety of cancer types across stages with a range of sensitivity, specificity, and ability to predict the origin of the cancer signal. However, little is known about the general US population's preferences for MCED tests.ObjectiveTo quantify preferences for MCED tests among US adults aged 50-80 years using a discrete choice experiment (DCE).MethodsTo quantify preferences for attributes of blood-based MCED tests, an online DCE was conducted with five attributes (true positives, false negatives, false positives, likelihood of the cancer type unknown, number of cancer types detected), among the US population aged 50-80 years recruited via online panels and social media. Data were analyzed using latent class multinomial logit models and relative attribute importance was obtained.ResultsParticipants (N = 1700) were 54% female, mean age 63.3 years. Latent class modeling identified three classes with distinct preferences for MCED tests. The rank order of attribute importance based on relative attribute importance varied by latent class, but across all latent classes, participants preferred higher accuracy (fewer false negatives and false positives, more true positives) and screenings that detected more cancer types and had a lower likelihood of cancer type unknown. Overall, 72% of participants preferred to receive an MCED test in addition to currently recommended cancer screenings.ConclusionsWhile there is significant heterogeneity in cancer screening preferences, the majority of participants preferred MCED screening and the accuracy of these tests is important. While the majority of participants preferred adding an MCED test to complement current cancer screenings, the latent class analyses identified a small (16%) and specific subset of individuals who value attributes differently, with particular concern regarding false-negative and false-positive test results, who are significantly less likely to opt-in.
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Project description:The present study examined why perirhinal cortex lesions in rats impair the spontaneous ability to select novel objects in preference to familiar objects, when both classes of object are presented simultaneously. The study began by repeating this standard finding, using a test of delayed object recognition memory. As expected, the perirhinal cortex lesions reduced the difference in exploration times for novel vs. familiar stimuli. In contrast, the same rats with perirhinal cortex lesions appeared to perform normally when the preferential exploration of novel vs. familiar objects was tested sequentially, i.e. when each trial consisted of only novel or only familiar objects. In addition, there was no indication that the perirhinal cortex lesions reduced total levels of object exploration for novel objects, as would be predicted if the lesions caused novel stimuli to appear familiar. Together, the results show that, in the absence of perirhinal cortex tissue, rats still receive signals of object novelty, although they may fail to link that information to the appropriate object. Consequently, these rats are impaired in discriminating the source of object novelty signals, leading to deficits on simultaneous choice tests of recognition.
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Project description:Previous work found a small but significant relationship between holistic processing measured with the composite task and face recognition ability measured by the Cambridge Face Memory Test (CFMT; Duchaine & Nakayama, 2006). Surprisingly, recent work using a different measure of holistic processing (Vanderbilt Holistic Face Processing Test [VHPT-F]; Richler, Floyd, & Gauthier, 2014) and a larger sample found no evidence for such a relationship. In Experiment 1 we replicate this unexpected result, finding no relationship between holistic processing (VHPT-F) and face recognition ability (CFMT). A key difference between the VHPT-F and other holistic processing measures is that unique face parts are used on each trial in the VHPT-F, unlike in other tasks where a small set of face parts repeat across the experiment. In Experiment 2, we test the hypothesis that correlations between the CFMT and holistic processing tasks are driven by stimulus repetition that allows for learning during the composite task. Consistent with our predictions, CFMT performance was correlated with holistic processing in the composite task when a small set of face parts repeated over trials, but not when face parts did not repeat. A meta-analysis confirms that relationships between the CFMT and holistic processing depend on stimulus repetition. These results raise important questions about what is being measured by the CFMT, and challenge current assumptions about why faces are processed holistically.
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Project description:Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.
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Project description:Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.
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Project description:The concept of holistic processing is a cornerstone of face-recognition research. In the study reported here, we demonstrated that holistic processing predicts face-recognition abilities on the Cambridge Face Memory Test and on a perceptual face-identification task. Our findings validate a large body of work that relies on the assumption that holistic processing is related to face recognition. These findings also reconcile the study of face recognition with the perceptual-expertise work it inspired; such work links holistic processing of objects with people's ability to individuate them. Our results differ from those of a recent study showing no link between holistic processing and face recognition. This discrepancy can be attributed to the use in prior research of a popular but flawed measure of holistic processing. Our findings salvage the central role of holistic processing in face recognition and cast doubt on a subset of the face-perception literature that relies on a problematic measure of holistic processing.
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