A causal relationship between face-patch activity and face-detection behavior

The primate brain contains distinct areas densely populated by face-selective neurons. One of these, face-patch ML, contains neurons selective for contrast relationships between face parts. Such contrast-relationships can serve as powerful heuristics for face detection. However, it is unknown whether neurons with such selectivity actually support face-detection behavior. Here, we devised a naturalistic face-detection task and combined it with fMRI-guided pharmacological inactivation of ML to test whether ML is of critical importance for real-world face detection. We found that inactivation of ML impairs face detection. The effect was anatomically specific, as inactivation of areas outside ML did not affect face detection, and it was categorically specific, as inactivation of ML impaired face detection while sparing body and object detection. These results establish that ML function is crucial for detection of faces in natural scenes, performing a critical first step on which other face processing operations can build.

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Face Detection in Natural Scenes

Perception ◽

10.1068/v96l1003 ◽

1996 ◽

Vol 25 (1_suppl) ◽

pp. 67-67 ◽

Cited By ~ 1

Author(s):

H Hill ◽

R Watt

Keyword(s):

Face Detection ◽

Face Processing ◽

Feature Detection ◽

Processing System ◽

Decision Task ◽

Natural Scenes ◽

Natural Scene ◽

Energy Models ◽

The Face ◽

And Performance

The first task of any face processing system is detection of the face. We studied how the human visual system achieves face detection using a 2AFC task in which subjects were required to detect a face in the image of a natural scene. Luminance noise was added to the stimuli and performance was measured as a function of orientation and orientation bandwidth of the noise. Sensitivity levels and the effects of orientation bandwidth were similar for horizontally and vertically oriented noise. Performance was reduced for the smallest orientation bandwidth (5.6°) noise but sensitivity did not decrease further with increasing bandwidth until a point between 45° and 90°. The results suggest that important information may be oriented close to the vertical and horizontal. To test whether the results were specific to the task of face detection the same noise was added to the images in a man-made natural decision task. Equivalent levels of noise were found to be more disruptive and the effect of orientation bandwidth was different. The results are discussed in terms of models of face processing making use of oriented filters (eg Watt and Dakin, 1993 Perception22 Supplement, 12) and local energy models of feature detection (Morrone and Burr, 1988 Proceedings of the Royal Society of London B235 221 – 245).

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Chromatic Cues for Face Detection in Natural Scenes

Perception ◽

10.1068/v970316 ◽

1997 ◽

Vol 26 (1_suppl) ◽

pp. 294-294

Author(s):

A Oliva ◽

S Akamatsu ◽

P G Schyns

Keyword(s):

Face Detection ◽

Reaction Times ◽

Detection Task ◽

Natural Scenes ◽

Colour Space ◽

Affect Detection ◽

Detection Of Objects ◽

Chromatic Cues ◽

Colour Histograms ◽

Euclidian Distances

One of the challenging problems of human and machine vision is the detection of objects against complex backgrounds. Our research addresses the question of how faces can be very quickly detected in naturalistic scenes on the basis of luminance and chromatic cues. Although luminance information varies with pose and illumination differences, chromatic information is by and large invariant under these transformations. Hence, chromatic information might be a very powerful cue for segmentation and detection. We compared faces of different pigmentation against background scenes of different colours. Specifically, colour histograms were computed in a perceptually uniform colour space (L*u*v*). We computed the Euclidian distances between the averages of the colour histograms of faces and scenes in L*u*v*. This metric was used to calibrate the contrast between face and scene colour in the experimental design. In a face detection task, subjects saw faces against scene backgrounds at a different distance in colour space. Each combination face - scene was presented for 120 ms (to prevent saccadic explorations), and the subject's task was to indicate whether or not a face was present. Controls involved face - scene pairs on an isoluminant background. Results revealed that luminance information did not affect detection on the basis of chromatic cues. Importantly, the metric of detectability in L*u*v* space between scene and faces predicted reaction times to detection.

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Low-level image statistics in natural scenes influence perceptual decision-making

10.31234/osf.io/p3r8a ◽

2018 ◽

Author(s):

Noor Seijdel ◽

Sara Jahfari ◽

Iris I.A. Groen ◽

H. Steven Scholte

Keyword(s):

Decision Making ◽

Real World ◽

Detection Task ◽

Natural Scenes ◽

Natural Scene ◽

Natural Scene Statistics ◽

Perceptual Decision Making ◽

Perceptual Decision ◽

Low Level ◽

Animal Detection

A fundamental component of interacting with our environment is gathering and interpretation of sensory information. When investigating how perceptual information shapes the mechanisms of decision-making, most researchers have relied on the use of manipulated or unnatural information as perceptual input, resulting in findings that may not generalize to real-world scenes. Unlike simplified, artificial stimuli, real-world scenes contain low-level regularities (natural scene statistics) that are informative about the structural complexity of a scene, which the brain could exploit during perceptual decision-making. In this study, participants performed an animal detection task on low, medium or high complexity scenes as determined by two biologically plausible natural scene statistics, contrast energy (CE) or spatial coherence (SC). In experiment 1, stimuli were sampled such that CE and SC both influenced scene complexity. Diffusion modeling showed that both the speed of information processing and the required evidence were affected by the low-level scene complexity. Experiment 2a/b refined these observations by showing how the isolated manipulation of SC alone resulted in weaker but comparable effects on decision-making, whereas the manipulation of only CE had no effect. Overall, performance was best for scenes with intermediate complexity. Our systematic definition of natural scene statistics quantifies how complexity of natural scenes interacts with decision-making in an animal detection task. We speculate that the computation of CE and SC could serve as an indication to adjust perceptual decision-making based on the complexity of the input.

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Generating Images with Physics-Based Rendering for an Industrial Object Detection Task: Realism versus Domain Randomization

Sensors ◽

10.3390/s21237901 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7901

Author(s):

Leon Eversberg ◽

Jens Lambrecht

Keyword(s):

Object Detection ◽

Real World ◽

Domain Knowledge ◽

Industrial Applications ◽

Detection Task ◽

Training Data ◽

Industrial Object ◽

Versus Domain ◽

Promising Solution ◽

Background Object

Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, minimizing the domain gap between synthetic and real-world images remains a problem. Therefore, based on a real-world application, we explored the generation of images with physics-based rendering for an industrial object detection task. Setting up the render engine’s environment requires a lot of choices and parameters. One fundamental question is whether to apply the concept of domain randomization or use domain knowledge to try and achieve photorealism. To answer this question, we compared different strategies for setting up lighting, background, object texture, additional foreground objects and bounding box computation in a data-centric approach. We compared the resulting average precision from generated images with different levels of realism and variability. In conclusion, we found that domain randomization is a viable strategy for the detection of industrial objects. However, domain knowledge can be used for object-related aspects to improve detection performance. Based on our results, we provide guidelines and an open-source tool for the generation of synthetic images for new industrial applications.

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How Native Background Affects Human Performance in Real-World Visual Object Detection: An Event-Related Potential Study

Frontiers in Neuroscience ◽

10.3389/fnins.2021.665084 ◽

2021 ◽

Vol 15 ◽

Author(s):

Yue Wang ◽

Jianpu Yan ◽

Zhongliang Yin ◽

Shenghan Ren ◽

Minghao Dong ◽

...

Keyword(s):

Object Detection ◽

Real World ◽

Visual Processing ◽

Event Related Potentials ◽

Event Related Potential ◽

Visual Object ◽

Detection Accuracy ◽

Natural Scenes ◽

Experimental Conditions ◽

Related Potentials

Visual processing refers to the process of perceiving, analyzing, synthesizing, manipulating, transforming, and thinking of visual objects. It is modulated by both stimulus-driven and goal-directed factors and manifested in neural activities that extend from visual cortex to high-level cognitive areas. Extensive body of studies have investigated the neural mechanisms of visual object processing using synthetic or curated visual stimuli. However, synthetic or curated images generally do not accurately reflect the semantic links between objects and their backgrounds, and previous studies have not provided answers to the question of how the native background affects visual target detection. The current study bridged this gap by constructing a stimulus set of natural scenes with two levels of complexity and modulating participants' attention to actively or passively attend to the background contents. Behaviorally, the decision time was elongated when the background was complex or when the participants' attention was distracted from the detection task, and the object detection accuracy was decreased when the background was complex. The results of event-related potentials (ERP) analysis explicated the effects of scene complexity and attentional state on the brain responses in occipital and centro-parietal areas, which were suggested to be associated with varied attentional cueing and sensory evidence accumulation effects in different experimental conditions. Our results implied that efficient visual processing of real-world objects may involve a competition process between context and distractors that co-exist in the native background, and extensive attentional cues and fine-grained but semantically irrelevant scene information were perhaps detrimental to real-world object detection.

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Face Detection for Real World Application

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) ◽

10.1109/iciem51511.2021.9445287 ◽

2021 ◽

Author(s):

Dilpreet Singh Brar ◽

Amit Kumar ◽

Pallavi ◽

Usha Mittal ◽

Pooja Rana

Keyword(s):

Face Detection ◽

Real World ◽

Real World Application

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Mandatory First Impressions: Happy Expressions Increase Trustworthiness Ratings of Subsequent Neutral Images

Perception ◽

10.1177/0301006620987205 ◽

2021 ◽

Vol 50 (2) ◽

pp. 103-115

Author(s):

S. M. Thierry ◽

A. C. Twele ◽

C. J. Mondloch

Keyword(s):

Multilevel Modeling ◽

Facial Expressions ◽

Real World ◽

Face Processing ◽

First Impressions ◽

Automatic Process ◽

Behavioral Consequences

First impressions of traits are formed rapidly and nonconsciously, suggesting an automatic process. We examined whether first impressions of trustworthiness are mandatory, another component of automaticity in face processing. In Experiment 1a, participants rated faces displaying subtle happy, subtle angry, and neutral expressions on trustworthiness. Happy faces were rated as more trustworthy than neutral faces; angry faces were rated as less trustworthy. In Experiment 1b, participants learned eight identities, half showing subtle happy and half showing subtle angry expressions. They then rated neutral images of these same identities (plus four novel neutral faces) on trustworthiness. Multilevel modeling analyses showed that identities previously shown with subtle expressions of happiness were rated as more trustworthy than novel identities. There was no effect of previously seen subtle angry expressions on ratings of trustworthiness. Mandatory first impressions based on subtle facial expressions were also reflected in two ratings designed to assess real-world outcomes. Participants indicated that they were more likely to vote for identities that had posed happy expressions and more likely to loan them money. These findings demonstrate that first impressions of trustworthiness based on previously seen subtle happy, but not angry, expressions are mandatory and are likely to have behavioral consequences.

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