Critical features for face recognition

Abudarham, Naphtali; Shkiller, Lior; Yovel, Galit

doi:10.1016/j.cognition.2018.09.002

Cited by 94 publications

(70 citation statements)

References 28 publications

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“…critical features) are perceived as changes in identity and those features tend to be invariant for different tokens of the same identity. Interestingly, Abudarham et al (2019) showed that the OpenFace algorithm that we used in the present study also seemed to be capturing those same critical features. Given our results in right FFA, it would be interesting to see whether representations in this region can also distinguish between the processing of the critical and non-critical face features as described by Abudarham and colleagues (2016;2019).…”

Section: Discussionsupporting

confidence: 56%

“…Rhodes, 1988;Calder et al, 2001;Yovel & Duchaine, 2006;Tardif et al, 2019). In particular, Abudarham and Yovel (2016) recently showed that features such as lip thickness, hair colour, eye colour, eye shape, and eyebrow thickness were crucial in distinguishing between individuals (see also Abudarham et al, 2019). Additionally, we perceive a vast amount of sociallyrelevant information from faces that can be used to distinguish between different individuals, such as gender, age, ethnicity, social traits (Oosterhof & Todorov, 2008;Sutherland et al 2013), and even relationships and social network position (Parkinson et al, 2014;.…”

Section: Introductionmentioning

confidence: 98%

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FFA and OFA encode distinct types of face identity information

Tsantani

Kriegeskorte

Storrs

et al. 2020

Preprint

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Faces of different people elicit distinct functional MRI (fMRI) patterns in several face-selective brain regions. Here we used representational similarity analysis to investigate what type of identitydistinguishing information is encoded in three face-selective regions: fusiform face area (FFA), occipital face area (OFA), and posterior superior temporal sulcus (pSTS). We used fMRI to measure brain activity patterns elicited by naturalistic videos of famous face identities, and compared their representational distances in each region with models of the differences between identities. Models included low-level to high-level image-computable properties and complex human-rated properties. We found that the FFA representation reflected perceived face similarity, social traits, and gender, and was well accounted for by the OpenFace model (deep neural network, trained to cluster faces by identity). The OFA encoded low-level image-based properties (pixel-wise and Gabor-jet dissimilarities). Our results suggest that, although FFA and OFA can both discriminate between identities, the FFA representation is further removed from the image, encoding higher-level perceptual and social face information.

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Section: Discussionsupporting

confidence: 56%

Section: Introductionmentioning

confidence: 98%

FFA and OFA encode distinct types of face identity information

Tsantani

Kriegeskorte

Storrs

et al. 2020

Preprint

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“…In order to examine whether a DCNN generated a human-like, view-invariant representation across its different layers, we used two measures of human view-invariant face recognition: First, we examined how similar are the representations of face identity across different head views (Study 1). Second, we examined the sensitivity of the DCNNs to view-invariant facial features that are used by humans for face recognition (8)(9)(10). For both measures, which will be described in more details below, we examined the representation of DCNNs trained to recognize faces or objects, across their layers, and compared them to the representation generated by humans.…”

Section: Discussionmentioning

confidence: 99%

“…In a series of previous studies, we discovered a subset of view-invariant facial features that are used by humans to recognize faces (9,10,13). This subset of facial features includes the hair, lip-thickness, eye-color and shape and eyebrow-thickness.…”

Section: Studymentioning

confidence: 99%

Face recognition depends on specialized mechanisms tuned to view-invariant facial features: Insights from deep neural networks optimized for face or object recognition

Abudarham

Yovel

2020

Preprint

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SummaryFaces are processed by specialized neural mechanisms in high-level visual cortex. How does this divergence to a face-specific and an object-general system contribute to face recognition? Recent advances in machine face recognition together with our understanding of how humans recognize faces enable us to address this fundamental question. We hypothesized that face recognition depends on a face-selective system that is tuned to view-invariant facial features, which cannot be accomplished by an object-general system. To test this hypothesis, we used deep convolutional neural networks (DCNNs) that were optimized for face or object recognition. Consistent with the hierarchical architecture of the visual system, results show that a human-like, view-invariant face representation emerges only at higher layers of a face-trained but not the object-trained neural network. Thus, by combining human psychophysics and computational modelling of the visual system, we revealed how the functional organization of the visual cortex may contribute to recognition.

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“…These features are categorized into internal and external features respectively [140]. In this regard, [60] examined the role of high-PS and low-PS features in face recognition of familiar and unfamiliar faces and role of these critical features for DNN based face recognition. The review concluded that high-PS features are critical for human face recognition and are also used in DNN based trained on unconstrained faces.…”

Section: Face Recognitionmentioning

confidence: 99%

An analytical study of information extraction from unstructured and multidimensional big data

2019

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IntroductionInformation extraction (IE) process extracts useful structured information from the unstructured data in the form of entities, relations, objects, events and many other types. The extracted information from unstructured data is used to prepare data for analysis. Therefore, the efficient and accurate transformation of unstructured data in the IE process improves the data analysis. Numerous techniques have been introduced for different data types i.e. text, image, audio, and video.The advancement in technology promoted the rapid growth of data volume in recent years. The volume, variety (structured, unstructured, and semi-structured data) and velocity of big data have also changed the paradigm of computational capabilities of the systems. IBM estimated that more than 2.5 quintillion bytes of data are generated every Abstract Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Adnan and Akbar J Big Data (2019) 6:91 Malaysia Adnan and Akbar J Big Data (2019) 6:91 RESEARCHday. Among these statistics, it was also predicted that unstructured data from diverse sources will grow up to 90% in few years. IDC estimated that unstructured data will be 95% of the global data in 2020 with estimated 65% annual growth rate [1]. The common characteristics of unstructured data are, (i) it comes in multiple formats...

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Critical features for face recognition

Cited by 94 publications

References 28 publications

FFA and OFA encode distinct types of face identity information

FFA and OFA encode distinct types of face identity information

Face recognition depends on specialized mechanisms tuned to view-invariant facial features: Insights from deep neural networks optimized for face or object recognition

An analytical study of information extraction from unstructured and multidimensional big data

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