Prototype Face Research Articles

VoiceStyle: Voice-Based Face Generation via Cross-Modal Prototype Contrastive Learning

Can we predict a person’s appearance solely based on their voice? This article explores this question by focusing on generating a face from an unheard voice segment. Our proposed method, VoiceStyle, combines cross-modal representation learning with generation modeling, enabling us to incorporate voice semantic cues into the generated face. In the first stage, we introduce cross-modal prototype contrastive (CMPC) learning to establish the association between voice and face. Recognizing the presence of false negative and deviate positive instances in real-world unlabeled data, we not only use voice–face pairs in the same video but also construct additional semantic positive pairs through unsupervised clustering, enhancing the learning process. Moreover, we recalibrate instances based on their similarity to cluster centers in the other modality. In the second stage, we harness the powerful generative capabilities of StyleGAN to produce faces. We optimize the latent code in StyleGAN’s latent space, guided by the learned voice–face alignment. To address the importance of selecting an appropriate starting point for optimization, we aim to automatically find an optimal starting point by utilizing the face prototype derived from the voice input. The entire pipeline can be implemented in a self-supervised manner, eliminating the need for manually labeled annotations. Through extensive experiments, we demonstrate the effectiveness and performance of our VoiceStyle method in both cross-modal representation learning and voice-based face generation.

The many faces of compensation: The similarities and differences between social and facial models of perception.

Previous research shows that stereotypes can distort the visual representation of groups in a top-down fashion. In the present endeavor, we tested if the compensation effect-the negative relationship that emerges between the social dimensions of warmth and competence when judging two social targets-would bias the visual representations of these targets in a compensatory way. We captured participants' near spontaneous facial prototypes of social targets by means of an unconstrained technique, namely the reverse correlation. We relied on a large multi-phase study (N = 869) and found that the expectations of the facial content of two novel groups that differed on one of the two social dimensions are biased in a compensatory manner on the facial dimensions of trustworthiness, warmth, and dominance but not competence. The present research opens new avenues by showing that compensation not only manifests itself on abstract ratings but that it also orients the visual representations of social targets.

Decoding Angry and Disgusted Faces Across Cultures: Facial Prototypes and Software Matter

Decoding Angry and Disgusted Faces Across Cultures: Facial Prototypes and Software Matter

Not the norm: Face likeness is not the same as similarity to familiar face prototypes.

Not the norm: Face likeness is not the same as similarity to familiar face prototypes.

Newton’s Law of Gravitational Force (NLGF) based Machine Learning Technique for Uneven Illuminated Face Detection

A photo gallery is crucial for organizing your photos, presenting them in beautiful categories, and doing sophisticated memory searches. The photo gallery is portrayed in a vocabulary of nonlinear similarities to the prototype face image collection. One of the difficult research ideas for machine learning technologies is the maintenance of a photo gallery using facial recognition. Based on changes in the faces' appearance, faces are identified. This research proposes novel machine learning algorithms to recognize faces by characterizing the majority of discriminating local characteristics, which maximizes the dissimilarity between face photos of different persons and reduces the dissimilarity between features between face images of the same person. This method relies on Newton's third law of gravitational force to determine the relationship between pixels to extract the features of noisy accurately and efficiently, unevenly illuminated, and rotationally invariant face images.

King Lear, Answer to Job: The Archetypes of Godhead

William Shakespeare’s King Lear is often deemed the penultimate tragic drama. To fully grasp its archetypal reach, we must apply the insights of Carl Jung. Before Lear gives away his kingdom, he is as arbitrarily willful and irrational as the intemperate Yahweh whom Jung portrays in Answer to Job. Yahweh is a primitive, unconscious storm deity who must redeem himself from the injustices he inflicted upon Job by suffering the common lot of humanity as Jesus. Jung saw in the Bible a story of Yahweh’s struggle to gain Job’s humane consciousness. Shakespeare, the visionary artist, adopts this same basic narrative when Lear in the storm scene becomes the suffering Job, then gains insight and compassion with his Fool and later with his daughter, Cordelia. When Cordelia dies, Lear responds with Promethean defiance. Shakespeare presents his own vision of Jung’s quaternal Godhead with Lear first playing Yahweh, then suffering Job, and finally, Job’s Promethean opposite. The prototypical face of Godhead, the Wise-figure archetype embodying truth and wholeness, is Cordelia, who manifests both Christ as a symbol of the self and Sophia as Holy Spirit. In this way, Shakespeare solves Jung’s problem of the Godhead’s missing feminine. As a literary treatment of the Godhead’s archetypes and tragic nature, King Lear is a singularly important work. By illumining Lear’s archetypal patterns, Answer to Job demonstrates the depth and profundity of Jung’s religious insights on the Godhead.

Not the norm: Face likeness is not the same as similarity to familiar face prototypes.

Face images depicting the same individual can differ substantially from one another. Ecological variation in pose, expression, lighting, and other sources of appearance variability complicates the recognition and matching of unfamiliar faces, but acquired familiarity leads to the ability to cope with these challenges. Among the many ways that face of the same individual can vary, some images are judged to be better likenesses of familiar individuals than others. Simply put, these images look more like the individual under consideration than others. But what does it mean for an image to be a better likeness than another? Does likeness entail typicality, or is it something distinct from this? We examined the relationship between the likeness of face images and the similarity of those images to average images of target individuals using a set of famous faces selected for reciprocal familiarity/unfamiliarity across US and UK participants. We found that though likeness judgments are correlated with similarity-to-prototype judgments made by both familiar and unfamiliar participants, this correlation was smaller than the correlation between similarity judgments made by different participant groups. This implies that while familiarity weakens the relationship between likeness and similarity-to-prototype judgments, it does not change similarity-to-prototype judgments to the same degree.

Prototype Face Attendance System Berbasis Raspberry PI Menggunakan Metode Eigenface di Program Studi Pendidikan Vokasional Teknik Elektro Untirta

Face recognition is one of the pattern recognition approaches for the purposes of identifying a person's face with a biometric approach. From this biometric pattern, it can be used as a means to identify the presence of a student. Assisted by the Raspberry Pi, this facial recognition can be done and it can be concluded whether the biometric pattern matches or not. This suitability will be a reference to indicate the presence of a student. So a Raspberry Pi-based Prototype Face Attendance System was made using the Eigenface method. Eigenface is an algorithm that works based on Principle Component Analysis (PCA). Tests were carried out by testing 30 students with a total of 30 photo samples per individual resulting in an accuracy value of 78.85% and error value is 21.15%.

Cloth face mask fit and function for children part two: Material Selection

The second component of this three-paper series studying cloth face masks for children ages 4 to 6 years old concentrates on optimizing aerosol capture and air permeability through fabric selection. Material choices were evaluated in two modes: Flat Filter (FF) and Head Form (HF). FF isolates material factors while HF simulates the performance of the constructed masks on a 3D printed child head form. In FF mode, higher filtration efficiency correlated to lower air permeability in both reusable commercial and experimental face masks regardless of fiber contents, fabric structures, and number of layers. Our prototype face mask developed in design exploration successfully captured 37 ± 12% of 0.3 μm, 87 ± 3% of 0.5 μm, and 87 ± 2% of 1.0 μm particles while maintaining good air permeability, moisture capture, and aerosolized salt capture in HF mode. Sealing masks to minimize outward leakage reduced particle capture up to 64%. Particle leakage data captured at the eye level of the head form illuminated the synergy between mask design, material choice, and fit.

Impact of face outline, parafoveal feature number and feature type on early face perception in a gaze-contingent paradigm: A mass-univariate re-analysis of ERP data

Recent ERP research using a gaze-contingent paradigm suggests the face-sensitive N170 component is modulated by the presence of a face outline, the number of parafoveal facial features, and the type of feature in parafovea (Parkington and Itier, 2019). The present study re-analyzed these data using robust mass univariate statistics available through the LIMO toolbox, allowing the examination of the ERP signal across all electrodes and time points. We replicated the finding that the presence of a face outline significantly reduced ERP latencies and amplitudes, suggesting it is an important cue to the prototypical face template. However, we found that this effect began around 114 ms, and was maximal during the P1-N170 and N170-P2 intervals. The number of features present in parafovea also impacted the entire waveform, with systematic reductions in amplitude and latency as the number of features increased. This effect was maximal around 120 ms during the P1-N170 interval and around 170 ms between the N170 and P2. The ERP response was also modulated by feature type; contrary to previous findings this effect was maximal around 200 ms and the P2 peak. Although we provide partial replication of the previous results on the N170, the effects were more temporally distributed in the present analysis. These effects were generally maximal before and after the N170 and were the weakest at the N170 peak itself. This re-analysis demonstrates that classical ERP analysis can obscure important aspects of face processing beyond the N170 peak, and that tools like mass univariate statistics are needed to shed light on the whole time-course of face processing.

DESIGNING A MASKMAN BUSINESS MODEL: CLAY FACE MASK FOR MEN

Based on the preliminary survey, there was a market need for skin care for men, one of which is face masks. There were limitations to the choice of face-mask products for men in Indonesia. This research aimed to design a verified business model canvas based on the solutions offered to solve consumer problems with face mask products. This research used the customer development method focused on the customer discovery stage and was conducted from January to August 2020 through online questionnaires. The final result of this research is a solution in the form of a prototype face mask product specifically for men following consumer problems and a verified business model canvas. This study presented charcoal clay masks, which have detoxifying benefits to treat and maintain skin health for men. The consumer segment consisted of men with an age range of 20-29 years who live in Jabodetabek. The solution offered by MaskMan has solved consumer problems with face mask products specifically for men. The final sales channel made use of Instagram, Tokopedia, and exhibitions. Based on the test results, consumers needed MaskMan products and were willing to pay for products with a price range of IDR 50,000 - IDR 100,000. The selling price of MaskMan products was IDR 66,500.

A Facial Recognition System

Abstract: This paper introduces the design, implementation, and validation of a Digital Signal Processor (DSP) -based Prototype face recognition and authentication system. This system is designed to capture image sequences, detect facial features in photos, and detect and verify a person. The current application uses images captured on a webcam and compares them to archived websites using the Comprehensive Component Analysis (PCA) and Discrete Cosine Transform (DCT) methods. Initially, realtime verification of the captured images was performed using a PC-based program with algorithms developed in MATLAB. Next, the TMS320C6713DSP-based prototype system is upgraded and validated in real-time. Several tests are performed on different sets of images, and the performance and speed of the proposed system are measured in real-time. Finally, the result confirmed that the proposed system could be used in a variety of applications that are not possible in standard PC-based applications. Also, better results were seen from DCT analysis than PCA results.

The Progressive Values Scale: Assessing the Ideological Schism on the Left.

Progressivism has increasingly challenged traditional liberalism as the dominant influence within left-wing ideology. Across four studies, we developed a measure-the Progressive Values Scale (PVS)-that characterizes distinctly progressive values within the left-wing. In Study 1, left-wing participants evaluated divisive issues, with four scale factors emerging. In Study 2, we confirmed this factor structure and included a battery of personality and values measures to explore individual differences among those who maintain a progressive worldview. In Study 3, we achieved final confirmation of the factor structure and validated the ability of the PVS to assess a distinctly progressive perspective, insofar as progressives generated prototypical faces for Liberals and Conservatives that were markedly distinct from those generated by traditional liberals. In Study 4, we distinguished the PVS from measures of left-wing authoritarianism and demonstrated that it is a better predictor of progressive political preferences and social judgments.

Animal consciousness and phenomenal concepts

ABSTRACT A phenomenal concept is a concept that one possesses only if one has the relevant experience. In this essay, I argue that phenomenal concept theorists, namely, those who believe that we acquire phenomenal concepts through being acquainted with the relevant experience, can never succeed in determining which species of non-human animals are phenomenally conscious because they prohibit any a priori correlation between phenomenal and non-phenomenal concepts. I make my argument by first discussing several ways in which a phenomenal concept theorist may explain which animal species are conscious, namely, with neuro-identity theory, behaviorism, and functionalism, and the problem that these approaches entail. I then illustrate how the alternative approaches of scientific inference to the best explanation, analogical inference, similarity inference, and inference by prototype face the same problem.

Creation and Validation of the Japanese Cute Infant Face (JCIF) Dataset.

Research interest in cuteness perception and its effects on subsequent behavior and physiological responses has recently been increasing. The purpose of the present study was to produce a dataset of Japanese infant faces that are free of portrait rights and can be used for cuteness research. A total of 80 original facial images of 6-month-old infants were collected from their parents. The cuteness level of each picture was rated on a 7-point scale by 200 Japanese people (100 men and 100 women in their 20s–60s). Prototypical high- and low-cuteness faces were created by averaging the top 10 and bottom 10 faces according to the mean cuteness ratings. Then, 50 composite faces were made by mixing two faces randomly chosen from the 60 unused middle-cuteness faces. The normative cuteness ratings of these composite faces were obtained from 229 Japanese men and women in their 20s–60s. The shape of each composite face was transformed to be cuter (+50%) or less cute (–50%) along a continuum between the high- and low-cuteness prototypical faces. A two-alternative forced-choice task (N = 587) confirmed that cuteness discrimination was better than the chance level for all 50 face pairs. Moreover, the results showed that young men had poorer sensitivity to cuteness differences in infant faces than older men and women of any age. This Japanese Cute Infant Face (JCIF, “jay-sif”) dataset, including composite face images and normative rating scores, is publicly available online.

Encoding signs of orofacial neuropathic pain from facial expressions in mice

ObjectiveTo evaluate the effects of mental nerve injury in the facial reactions elicited by mechanical stimulation of different intensities and detect and quantify spontaneous facial pain-like expressions during a period free of stimuli, as signs of evoked and spontaneous pain in a mouse model for neuropathic orofacial pain. DesignWe recorded mouse heads in a fixed position during a stimulus-free period and with mechanical stimulation with 3 different Von Frey filaments. We extracted the Histograms of Oriented Gradients of each frame of the video recordings to be compared with a prototypical pain-like facial expression. The similarity score was then used to register and quantify the percentage of spontaneous pain-like facial reactions and evaluate the increased similarity to the prototypical pain-like face evoked by mechanical stimuli. The assessments were made one day before and four days after a unilateral mental nerve compression. ResultsOur findings show that mental nerve injury promotes an increase in spontaneous facial pain-like expressions and reduced mechanical threshold, reflected in a higher similarity to our pain-like face prototype, regardless of the intensity of the stimuli applied. ConclusionsMachine vision encodes the facial expression associated with evoked and spontaneous pain after mental nerve injury for up to four days. Facial expression quantitatively reflects the increased mechanical sensitivity elicited by mental nerve injury. We also show that this technique can detect spontaneous pain-like responses from facial reactions. Artificial vision can be applied to evaluate signs of orofacial neuropathic pain to study the involved neural circuits.

MC-GCN: A Multi-Scale Contrastive Graph Convolutional Network for Unconstrained Face Recognition With Image Sets.

In this paper, a Multi-scale Contrastive Graph Convolutional Network (MC-GCN) method is proposed for unconstrained face recognition with image sets, which takes a set of media (orderless images and videos) as a face subject instead of single media (an image or video). Due to factors such as illumination, posture, media source, etc., there are huge intra-set variances in a face set, and the importance of different face prototypes varies considerably. How to model the attention mechanism according to the relationship between prototypes or images in a set is the main content of this paper. In this work, we formulate a framework based on graph convolutional network (GCN), which considers face prototypes as nodes to build relations. Specifically, we first present a multi-scale graph module to learn the relationship between prototypes at multiple scales. Moreover, a Contrastive Graph Convolutional (CGC) block is introduced to build attention control model, which focuses on those frames with similar prototypes (contrastive information) between pair of sets instead of simply evaluating the frame quality. The experiments on IJB-A, YouTube Face, and an animal face dataset clearly demonstrate that our proposed MC-GCN outperforms the state-of-the-art methods significantly.

Methodological issues when using face prototypes: A case study on the Faceaurus dataset.

Prototype faces, created by averaging faces from several individuals sharing a common characteristic (for example a certain personality trait), can be used for highly informative experimental designs in face research. Although the facial prototype method is both ingenious and useful, we argue that its implementation is associated with three major issues: lack of external validity and non-independence of the units of information, both aggravated by a lack of transparency regarding the methods used and their limitations. Here, we describe these limitations and illustrate our claims with a systematic review of studies creating facial stimuli using the prototypes dataset 'Faceaurus'. We then propose some solutions that can eliminate or reduce these problems. We provide recommendations for future research employing this method on how to produce more generalisable and replicable results.

Face Identification based on Informative Knowledge Distillation using Morphological Model

This manuscript deals with face detection and recognition with the help of a morphological-based Informative knowledge distillation model. It suggests a face prototype compression technique via discriminatory knowledge distillation, that can thoroughly study human potential with two simultaneous tasks, age estimation as well as face verification, for facial pictures captured at varying ages with greatly reduced information. The detailed and rigorous experimental study helps to understand the involvement of many variables such as gender, age gap, race, and age group. We propose Informative Knowledge Distillation Using Morphological Model (IKDMM) which can draw from current face patterns the most discriminatory facial attributes, which may be utilized to oversee the training process for face models with low resolution; and complete extensive tests to demonstrate that the compressed pattern is able to obtain an extreme high identification rate with comparative precision with the latest high-resolution face patterns.

Implicit prototype learning in patients with memory deficit: Evidence from a study with Alzheimer's disease patients.

In a previous study (Zannino et al., 2012), it was demonstrated that individuals with amnestic mild cognitive impairment (MCI) were unimpaired on a new prototype learning task consisting of morphed faces (face prototype learning task [FPLT]). This paradigm was devised to improve on the classical dot pattern task by ruling out any reliance on residual episodic memory or working memory resources. In the present study, we aimed to demonstrate: first, that people with even more severe episodic memory impairment than MCI are unimpaired on a fully implicit prototype learning task and second, that the dot pattern task, at variance with the FPLT, requires a no negligible contribution from the episodic memory system. Twenty-four persons with Alzheimer's disease (AD) and 48 healthy controls took part in this experiment. As in the original study, in addition to the FPLT, they were also administered the classical dot pattern task and an ordinary forced-choice face recognition task. AD performed like normal controls in the FPLT but scored significantly worse on the dot pattern task and the face recognition task. Interestingly, although performance on the face recognition task did not correlate with that on the FPLT, a significant correlation was observed between the face recognition and the dot pattern task. Results support both of our claims: first, that also severe amnesic people can learn new visual prototypes with a fully implicit paradigm and, second, that the classical dot pattern task requires some degree of episodic resources. Further research is needed to rule out the role of working memory in solving the FPLT. (PsycInfo Database Record (c) 2022 APA, all rights reserved).